2023/12/15 - Amazon SageMaker Service - 1 new 49 updated api methods
Changes This release 1) introduces a new API: DeleteCompilationJob , and 2) adds InfraCheckConfig for Create/Describe training job API
Deletes the specified compilation job. This action deletes only the compilation job resource in Amazon SageMaker. It doesn't delete other resources that are related to that job, such as the model artifacts that the job creates, the compilation logs in CloudWatch, the compiled model, or the IAM role.
You can delete a compilation job only if its current status is COMPLETED , FAILED , or STOPPED . If the job status is STARTING or INPROGRESS , stop the job, and then delete it after its status becomes STOPPED .
See also: AWS API Documentation
Request Syntax
client.delete_compilation_job(
CompilationJobName='string'
)
string
[REQUIRED]
The name of the compilation job to delete.
None
{'AssociationType': {'SameAs'}}
Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking .
See also: AWS API Documentation
Request Syntax
client.add_association(
SourceArn='string',
DestinationArn='string',
AssociationType='ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced'|'SameAs'
)
string
[REQUIRED]
The ARN of the source.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the destination.
string
The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use.
ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job.
AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment.
DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs.
Produced - The source generated the destination. For example, a training job produced a model artifact.
dict
Response Syntax
{
'SourceArn': 'string',
'DestinationArn': 'string'
}
Response Structure
(dict) --
SourceArn (string) --
The ARN of the source.
DestinationArn (string) --
The Amazon Resource Name (ARN) of the destination.
{'ModelPackageSummaries': {'InferenceSpecification': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}}}
This action batch describes a list of versioned model packages
See also: AWS API Documentation
Request Syntax
client.batch_describe_model_package(
ModelPackageArnList=[
'string',
]
)
list
[REQUIRED]
The list of Amazon Resource Name (ARN) of the model package groups.
(string) --
dict
Response Syntax
{
'ModelPackageSummaries': {
'string': {
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 123,
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'InferenceSpecification': {
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalS3DataSource': {
'S3DataType': 'S3Object',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval'
}
},
'BatchDescribeModelPackageErrorMap': {
'string': {
'ErrorCode': 'string',
'ErrorResponse': 'string'
}
}
}
Response Structure
(dict) --
ModelPackageSummaries (dict) --
The summaries for the model package versions
(string) --
(dict) --
Provides summary information about the model package.
ModelPackageGroupName (string) --
The group name for the model package
ModelPackageVersion (integer) --
The version number of a versioned model.
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription (string) --
The description of the model package.
CreationTime (datetime) --
The creation time of the mortgage package summary.
InferenceSpecification (dict) --
Defines how to perform inference generation after a training job is run.
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) --
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) --
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) --
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
ModelPackageStatus (string) --
The status of the mortgage package.
ModelApprovalStatus (string) --
The approval status of the model.
BatchDescribeModelPackageErrorMap (dict) --
A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.
(string) --
(dict) --
The error code and error description associated with the resource.
ErrorCode (string) --
ErrorResponse (string) --
{'InferenceSpecification': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}},
'TrainingSpecification': {'SupportedTrainingInstanceTypes': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'ValidationSpecification': {'ValidationProfiles': {'TrainingJobDefinition': {'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}}}}}}
Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
See also: AWS API Documentation
Request Syntax
client.create_algorithm(
AlgorithmName='string',
AlgorithmDescription='string',
TrainingSpecification={
'TrainingImage': 'string',
'TrainingImageDigest': 'string',
'SupportedHyperParameters': [
{
'Name': 'string',
'Description': 'string',
'Type': 'Integer'|'Continuous'|'Categorical'|'FreeText',
'Range': {
'IntegerParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'ContinuousParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'CategoricalParameterRangeSpecification': {
'Values': [
'string',
]
}
},
'IsTunable': True|False,
'IsRequired': True|False,
'DefaultValue': 'string'
},
],
'SupportedTrainingInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
],
'SupportsDistributedTraining': True|False,
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'TrainingChannels': [
{
'Name': 'string',
'Description': 'string',
'IsRequired': True|False,
'SupportedContentTypes': [
'string',
],
'SupportedCompressionTypes': [
'None'|'Gzip',
],
'SupportedInputModes': [
'Pipe'|'File'|'FastFile',
]
},
],
'SupportedTuningJobObjectiveMetrics': [
{
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
],
'AdditionalS3DataSource': {
'S3DataType': 'S3Object',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip'
}
},
InferenceSpecification={
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalS3DataSource': {
'S3DataType': 'S3Object',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
ValidationSpecification={
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TrainingJobDefinition': {
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'HyperParameters': {
'string': 'string'
},
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'KeepAlivePeriodInSeconds': 123,
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
}
},
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
CertifyForMarketplace=True|False,
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the algorithm.
string
A description of the algorithm.
dict
[REQUIRED]
Specifies details about training jobs run by this algorithm, including the following:
The Amazon ECR path of the container and the version digest of the algorithm.
The hyperparameters that the algorithm supports.
The instance types that the algorithm supports for training.
Whether the algorithm supports distributed training.
The metrics that the algorithm emits to Amazon CloudWatch.
Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.
The input channels that the algorithm supports for training data. For example, an algorithm might support train , validation , and test channels.
TrainingImage (string) -- [REQUIRED]
The Amazon ECR registry path of the Docker image that contains the training algorithm.
TrainingImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
SupportedHyperParameters (list) --
A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>
(dict) --
Defines a hyperparameter to be used by an algorithm.
Name (string) -- [REQUIRED]
The name of this hyperparameter. The name must be unique.
Description (string) --
A brief description of the hyperparameter.
Type (string) -- [REQUIRED]
The type of this hyperparameter. The valid types are Integer , Continuous , Categorical , and FreeText .
Range (dict) --
The allowed range for this hyperparameter.
IntegerParameterRangeSpecification (dict) --
A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.
MinValue (string) -- [REQUIRED]
The minimum integer value allowed.
MaxValue (string) -- [REQUIRED]
The maximum integer value allowed.
ContinuousParameterRangeSpecification (dict) --
A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.
MinValue (string) -- [REQUIRED]
The minimum floating-point value allowed.
MaxValue (string) -- [REQUIRED]
The maximum floating-point value allowed.
CategoricalParameterRangeSpecification (dict) --
A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.
Values (list) -- [REQUIRED]
The allowed categories for the hyperparameter.
(string) --
IsTunable (boolean) --
Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
IsRequired (boolean) --
Indicates whether this hyperparameter is required.
DefaultValue (string) --
The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
SupportedTrainingInstanceTypes (list) -- [REQUIRED]
A list of the instance types that this algorithm can use for training.
(string) --
SupportsDistributedTraining (boolean) --
Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
MetricDefinitions (list) --
A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) -- [REQUIRED]
The name of the metric.
Regex (string) -- [REQUIRED]
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
TrainingChannels (list) -- [REQUIRED]
A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.
(dict) --
Defines a named input source, called a channel, to be used by an algorithm.
Name (string) -- [REQUIRED]
The name of the channel.
Description (string) --
A brief description of the channel.
IsRequired (boolean) --
Indicates whether the channel is required by the algorithm.
SupportedContentTypes (list) -- [REQUIRED]
The supported MIME types for the data.
(string) --
SupportedCompressionTypes (list) --
The allowed compression types, if data compression is used.
(string) --
SupportedInputModes (list) -- [REQUIRED]
The allowed input mode, either FILE or PIPE.
In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.
In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
(string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
SupportedTuningJobObjectiveMetrics (list) --
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
(dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables .
Type (string) -- [REQUIRED]
Whether to minimize or maximize the objective metric.
MetricName (string) -- [REQUIRED]
The name of the metric to use for the objective metric.
AdditionalS3DataSource (dict) --
The additional data source used during the training job.
S3DataType (string) -- [REQUIRED]
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) -- [REQUIRED]
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
dict
Specifies details about inference jobs that the algorithm runs, including the following:
The Amazon ECR paths of containers that contain the inference code and model artifacts.
The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.
The input and output content formats that the algorithm supports for inference.
Containers (list) -- [REQUIRED]
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) -- [REQUIRED]
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) -- [REQUIRED]
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) -- [REQUIRED]
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) -- [REQUIRED]
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
dict
Specifies configurations for one or more training jobs and that SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that SageMaker runs to test the algorithm's inference code.
ValidationRole (string) -- [REQUIRED]
The IAM roles that SageMaker uses to run the training jobs.
ValidationProfiles (list) -- [REQUIRED]
An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.
(dict) --
Defines a training job and a batch transform job that SageMaker runs to validate your algorithm.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
ProfileName (string) -- [REQUIRED]
The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
TrainingJobDefinition (dict) -- [REQUIRED]
The TrainingJobDefinition object that describes the training job that SageMaker runs to validate your algorithm.
TrainingInputMode (string) -- [REQUIRED]
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
HyperParameters (dict) --
The hyperparameters used for the training job.
(string) --
(string) --
InputDataConfig (list) -- [REQUIRED]
An array of Channel objects, each of which specifies an input source.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) -- [REQUIRED]
The name of the channel.
DataSource (dict) -- [REQUIRED]
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) -- [REQUIRED]
The file system id.
FileSystemAccessMode (string) -- [REQUIRED]
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) -- [REQUIRED]
The file system type.
DirectoryPath (string) -- [REQUIRED]
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) -- [REQUIRED]
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) -- [REQUIRED]
the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) -- [REQUIRED]
The resources, including the ML compute instances and ML storage volumes, to use for model training.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) -- [REQUIRED]
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) -- [REQUIRED]
Specifies the instance type of the instance group.
InstanceCount (integer) -- [REQUIRED]
Specifies the number of instances of the instance group.
InstanceGroupName (string) -- [REQUIRED]
Specifies the name of the instance group.
StoppingCondition (dict) -- [REQUIRED]
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
TransformJobDefinition (dict) --
The TransformJobDefinition object that describes the transform job that SageMaker runs to validate your algorithm.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) -- [REQUIRED]
A description of the input source and the way the transform job consumes it.
DataSource (dict) -- [REQUIRED]
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) -- [REQUIRED]
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) -- [REQUIRED]
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) -- [REQUIRED]
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) -- [REQUIRED]
Identifies the ML compute instances for the transform job.
InstanceType (string) -- [REQUIRED]
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) -- [REQUIRED]
The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
boolean
Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.
list
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'AlgorithmArn': 'string'
}
Response Structure
(dict) --
AlgorithmArn (string) --
The Amazon Resource Name (ARN) of the new algorithm.
{'AppType': {'Canvas',
'DatasetManager',
'DetailedProfiler',
'Local',
'RSession',
'SageMakerLite',
'Savitur',
'VSCode'}}
Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
See also: AWS API Documentation
Request Syntax
client.create_app(
DomainId='string',
UserProfileName='string',
SpaceName='string',
AppType='JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
AppName='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ResourceSpec={
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
}
)
string
[REQUIRED]
The domain ID.
string
The user profile name. If this value is not set, then SpaceName must be set.
string
The name of the space. If this value is not set, then UserProfileName must be set.
string
[REQUIRED]
The type of app.
string
[REQUIRED]
The name of the app.
list
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
Note
The value of InstanceType passed as part of the ResourceSpec in the CreateApp call overrides the value passed as part of the ResourceSpec configured for the user profile or the domain. If InstanceType is not specified in any of those three ResourceSpec values for a KernelGateway app, the CreateApp call fails with a request validation error.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
dict
Response Syntax
{
'AppArn': 'string'
}
Response Structure
(dict) --
AppArn (string) --
The Amazon Resource Name (ARN) of the app.
{'JupyterLabAppImageConfig': {'FileSystemConfig': {'DefaultGid': 'integer',
'DefaultUid': 'integer',
'MountPath': 'string'}}}
Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.
See also: AWS API Documentation
Request Syntax
client.create_app_image_config(
AppImageConfigName='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
KernelGatewayImageConfig={
'KernelSpecs': [
{
'Name': 'string',
'DisplayName': 'string'
},
],
'FileSystemConfig': {
'MountPath': 'string',
'DefaultUid': 123,
'DefaultGid': 123
}
},
JupyterLabAppImageConfig={
'FileSystemConfig': {
'MountPath': 'string',
'DefaultUid': 123,
'DefaultGid': 123
},
'ContainerConfig': {
'ContainerArguments': [
'string',
],
'ContainerEntrypoint': [
'string',
],
'ContainerEnvironmentVariables': {
'string': 'string'
}
}
}
)
string
[REQUIRED]
The name of the AppImageConfig. Must be unique to your account.
list
A list of tags to apply to the AppImageConfig.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
The KernelGatewayImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel will be shown to users before the image starts. Once the image runs, all kernels are visible in JupyterLab.
KernelSpecs (list) -- [REQUIRED]
The specification of the Jupyter kernels in the image.
(dict) --
The specification of a Jupyter kernel.
Name (string) -- [REQUIRED]
The name of the Jupyter kernel in the image. This value is case sensitive.
DisplayName (string) --
The display name of the kernel.
FileSystemConfig (dict) --
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
MountPath (string) --
The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
DefaultUid (integer) --
The default POSIX user ID (UID). If not specified, defaults to 1000 .
DefaultGid (integer) --
The default POSIX group ID (GID). If not specified, defaults to 100 .
dict
The JupyterLabAppImageConfig . You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in JupyterLab.
FileSystemConfig (dict) --
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
MountPath (string) --
The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
DefaultUid (integer) --
The default POSIX user ID (UID). If not specified, defaults to 1000 .
DefaultGid (integer) --
The default POSIX group ID (GID). If not specified, defaults to 100 .
ContainerConfig (dict) --
The configuration used to run the application image container.
ContainerArguments (list) --
The arguments for the container when you're running the application.
(string) --
ContainerEntrypoint (list) --
The entrypoint used to run the application in the container.
(string) --
ContainerEnvironmentVariables (dict) --
The environment variables to set in the container
(string) --
(string) --
dict
Response Syntax
{
'AppImageConfigArn': 'string'
}
Response Structure
(dict) --
AppImageConfigArn (string) --
The Amazon Resource Name (ARN) of the AppImageConfig.
{'AutoMLProblemTypeConfig': {'TextGenerationJobConfig': {'ModelAccessConfig': {'AcceptEula': 'boolean'}}}}
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
Note
CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.
CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob , as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).
Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2 .
For the list of available problem types supported by CreateAutoMLJobV2 , see AutoMLProblemTypeConfig .
You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2 .
See also: AWS API Documentation
Request Syntax
client.create_auto_ml_job_v2(
AutoMLJobName='string',
AutoMLJobInputDataConfig=[
{
'ChannelType': 'training'|'validation',
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
}
},
],
OutputDataConfig={
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
AutoMLProblemTypeConfig={
'ImageClassificationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
}
},
'TextClassificationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'ContentColumn': 'string',
'TargetLabelColumn': 'string'
},
'TimeSeriesForecastingJobConfig': {
'FeatureSpecificationS3Uri': 'string',
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'ForecastFrequency': 'string',
'ForecastHorizon': 123,
'ForecastQuantiles': [
'string',
],
'Transformations': {
'Filling': {
'string': {
'string': 'string'
}
},
'Aggregation': {
'string': 'sum'|'avg'|'first'|'min'|'max'
}
},
'TimeSeriesConfig': {
'TargetAttributeName': 'string',
'TimestampAttributeName': 'string',
'ItemIdentifierAttributeName': 'string',
'GroupingAttributeNames': [
'string',
]
},
'HolidayConfig': [
{
'CountryCode': 'string'
},
]
},
'TabularJobConfig': {
'CandidateGenerationConfig': {
'AlgorithmsConfig': [
{
'AutoMLAlgorithms': [
'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai',
]
},
]
},
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'FeatureSpecificationS3Uri': 'string',
'Mode': 'AUTO'|'ENSEMBLING'|'HYPERPARAMETER_TUNING',
'GenerateCandidateDefinitionsOnly': True|False,
'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
'TargetAttributeName': 'string',
'SampleWeightAttributeName': 'string'
},
'TextGenerationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'BaseModelName': 'string',
'TextGenerationHyperParameters': {
'string': 'string'
},
'ModelAccessConfig': {
'AcceptEula': True|False
}
}
},
RoleArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
SecurityConfig={
'VolumeKmsKeyId': 'string',
'EnableInterContainerTrafficEncryption': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
AutoMLJobObjective={
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
},
ModelDeployConfig={
'AutoGenerateEndpointName': True|False,
'EndpointName': 'string'
},
DataSplitConfig={
'ValidationFraction': ...
}
)
string
[REQUIRED]
Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
list
[REQUIRED]
An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type:
For tabular problem types: S3Prefix , ManifestFile .
For image classification: S3Prefix , ManifestFile , AugmentedManifestFile .
For text classification: S3Prefix .
For time-series forecasting: S3Prefix .
For text generation (LLMs fine-tuning): S3Prefix .
(dict) --
A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2 ).
ChannelType (string) --
The type of channel. Defines whether the data are used for training or validation. The default value is training . Channels for training and validation must share the same ContentType
Note
The type of channel defaults to training for the time-series forecasting problem type.
ContentType (string) --
The content type of the data from the input source. The following are the allowed content types for different problems:
For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
For image classification: image/png , image/jpeg , or image/* . The default value is image/* .
For text classification: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
CompressionType (string) --
The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None . If no compression type is provided, we default to None .
DataSource (dict) --
The data source for an AutoML channel (Required).
S3DataSource (dict) -- [REQUIRED]
The Amazon S3 location of the input data.
S3DataType (string) -- [REQUIRED]
The data type.
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. The S3Prefix should have the following format: s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. A ManifestFile should have the format shown below: [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"}, "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1", "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2", ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2 ). Here is a minimal, single-record example of an AugmentedManifestFile : {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg", "label-metadata": {"class-name": "cat" } For more information on AugmentedManifestFile , see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File .
S3Uri (string) -- [REQUIRED]
The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.
dict
[REQUIRED]
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
KmsKeyId (string) --
The Key Management Service (KMS) encryption key ID.
S3OutputPath (string) -- [REQUIRED]
The Amazon S3 output path. Must be 128 characters or less.
dict
[REQUIRED]
Defines the configuration settings of one of the supported problem types.
ImageClassificationJobConfig (dict) --
Settings used to configure an AutoML job V2 for the image classification problem type.
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
TextClassificationJobConfig (dict) --
Settings used to configure an AutoML job V2 for the text classification problem type.
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
ContentColumn (string) -- [REQUIRED]
The name of the column used to provide the sentences to be classified. It should not be the same as the target column.
TargetLabelColumn (string) -- [REQUIRED]
The name of the column used to provide the class labels. It should not be same as the content column.
TimeSeriesForecastingJobConfig (dict) --
Settings used to configure an AutoML job V2 for the time-series forecasting problem type.
FeatureSpecificationS3Uri (string) --
A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig . When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig . If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig .
You can input FeatureAttributeNames (optional) in JSON format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] } .
You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Autopilot supports the following data types: numeric , categorical , text , and datetime .
Note
These column keys must not include any column set in TimeSeriesConfig .
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
ForecastFrequency (string) -- [REQUIRED]
The frequency of predictions in a forecast.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, 1D indicates every day and 15min indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min .
The valid values for each frequency are the following:
Minute - 1-59
Hour - 1-23
Day - 1-6
Week - 1-4
Month - 1-11
Year - 1
ForecastHorizon (integer) -- [REQUIRED]
The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.
ForecastQuantiles (list) --
The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.
(string) --
Transformations (dict) --
The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.
Filling (dict) --
A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic. You can specify multiple filling methods for a single column.
The supported filling methods and their corresponding options are:
frontfill : none (Supported only for target column)
middlefill : zero , value , median , mean , min , max
backfill : zero , value , median , mean , min , max
futurefill : zero , value , median , mean , min , max
To set a filling method to a specific value, set the fill parameter to the chosen filling method value (for example "backfill" : "value" ), and define the filling value in an additional parameter prefixed with "_value". For example, to set backfill to a value of 2 , you must include two parameters: "backfill": "value" and "backfill_value":"2" .
(string) --
(dict) --
(string) --
(string) --
Aggregation (dict) --
A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.
The supported aggregation methods are sum (default), avg , first , min , max .
Note
Aggregation is only supported for the target column.
(string) --
(string) --
TimeSeriesConfig (dict) -- [REQUIRED]
The collection of components that defines the time-series.
TargetAttributeName (string) -- [REQUIRED]
The name of the column representing the target variable that you want to predict for each item in your dataset. The data type of the target variable must be numerical.
TimestampAttributeName (string) -- [REQUIRED]
The name of the column indicating a point in time at which the target value of a given item is recorded.
ItemIdentifierAttributeName (string) -- [REQUIRED]
The name of the column that represents the set of item identifiers for which you want to predict the target value.
GroupingAttributeNames (list) --
A set of columns names that can be grouped with the item identifier column to create a composite key for which a target value is predicted.
(string) --
HolidayConfig (list) --
The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.
(dict) --
Stores the holiday featurization attributes applicable to each item of time-series datasets during the training of a forecasting model. This allows the model to identify patterns associated with specific holidays.
CountryCode (string) --
The country code for the holiday calendar.
For the list of public holiday calendars supported by AutoML job V2, see Country Codes . Use the country code corresponding to the country of your choice.
TabularJobConfig (dict) --
Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).
CandidateGenerationConfig (dict) --
The configuration information of how model candidates are generated.
AlgorithmsConfig (list) --
Stores the configuration information for the selection of algorithms used to train model candidates on tabular data.
The list of available algorithms to choose from depends on the training mode set in ` TabularJobConfig.Mode https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TabularJobConfig.html`__ .
AlgorithmsConfig should not be set in AUTO training mode.
When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig .
For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.
(dict) --
The collection of algorithms run on a dataset for training the model candidates of an Autopilot job.
AutoMLAlgorithms (list) -- [REQUIRED]
The selection of algorithms run on a dataset to train the model candidates of an Autopilot job.
Note
Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING ). Choose a minimum of 1 algorithm.
In ENSEMBLING mode:
"catboost"
"extra-trees"
"fastai"
"lightgbm"
"linear-learner"
"nn-torch"
"randomforest"
"xgboost"
In HYPERPARAMETER_TUNING mode:
"linear-learner"
"mlp"
"xgboost"
(string) --
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
FeatureSpecificationS3Uri (string) --
A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] } .
You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Note
These column keys may not include the target column.
In ensembling mode, Autopilot only supports the following data types: numeric , categorical , text , and datetime . In HPO mode, Autopilot can support numeric , categorical , text , datetime , and sequence .
If only FeatureDataTypes is provided, the column keys (col1 , col2 ,..) should be a subset of the column names in the input data.
If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames .
The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.
Mode (string) --
The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO . In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
GenerateCandidateDefinitionsOnly (boolean) --
Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
ProblemType (string) --
The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see Amazon SageMaker Autopilot problem types .
Note
You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.
TargetAttributeName (string) -- [REQUIRED]
The name of the target variable in supervised learning, usually represented by 'y'.
SampleWeightAttributeName (string) --
If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation .
Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
Support for sample weights is available in Ensembling mode only.
TextGenerationJobConfig (dict) --
Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type.
Note
The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions .
CompletionCriteria (dict) --
How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
BaseModelName (string) --
The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot . If no BaseModelName is provided, the default model used is Falcon7BInstruct .
TextGenerationHyperParameters (dict) --
The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters .
"epochCount" : The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".
"batchSize" : The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".
"learningRate" : The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".
"learningRateWarmupSteps" : The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".
Here is an example where all four hyperparameters are configured.
{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }
(string) --
(string) --
ModelAccessConfig (dict) --
The access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . For more information, see End-user license agreements .
AcceptEula (boolean) -- [REQUIRED]
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
string
[REQUIRED]
The ARN of the role that is used to access the data.
list
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources . Tag keys must be unique per resource.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
The security configuration for traffic encryption or Amazon VPC settings.
VolumeKmsKeyId (string) --
The key used to encrypt stored data.
EnableInterContainerTrafficEncryption (boolean) --
Whether to use traffic encryption between the container layers.
VpcConfig (dict) --
The VPC configuration.
SecurityGroupIds (list) -- [REQUIRED]
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) -- [REQUIRED]
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
dict
Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective .
Note
For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType ), or none at all.
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot .
MetricName (string) -- [REQUIRED]
The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
For tabular problem types:
List of available metrics:
Regression: InferenceLatency , MAE , MSE , R2 , RMSE
Binary classification: Accuracy , AUC , BalancedAccuracy , F1 , InferenceLatency , LogLoss , Precision , Recall
Multiclass classification: Accuracy , BalancedAccuracy , F1macro , InferenceLatency , LogLoss , PrecisionMacro , RecallMacro
For a description of each metric, see Autopilot metrics for classification and regression .
Default objective metrics:
Regression: MSE .
Binary classification: F1 .
Multiclass classification: Accuracy .
For image or text classification problem types:
List of available metrics: Accuracy For a description of each metric, see Autopilot metrics for text and image classification .
Default objective metrics: Accuracy
For time-series forecasting problem types:
List of available metrics: RMSE , wQL , Average wQL , MASE , MAPE , WAPE For a description of each metric, see Autopilot metrics for time-series forecasting .
Default objective metrics: AverageWeightedQuantileLoss
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot .
dict
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
AutoGenerateEndpointName (boolean) --
Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False .
Note
If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; otherwise a 400 error is thrown.
EndpointName (string) --
Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
Note
Specify the EndpointName if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error is thrown.
dict
This structure specifies how to split the data into train and validation datasets.
The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob , the validation dataset must be less than 2 GB in size.
Note
This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.
ValidationFraction (float) --
The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.
dict
Response Syntax
{
'AutoMLJobArn': 'string'
}
Response Structure
(dict) --
AutoMLJobArn (string) --
The unique ARN assigned to the AutoMLJob when it is created.
{'OutputConfig': {'TargetDevice': {'ml_c6g', 'ml_m6g', 'rasp4b'}}}
Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.
If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.
In the request body, you provide the following:
A name for the compilation job
Information about the input model artifacts
The output location for the compiled model and the device (target) that the model runs on
The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.
You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.
To stop a model compilation job, use StopCompilationJob . To get information about a particular model compilation job, use DescribeCompilationJob . To get information about multiple model compilation jobs, use ListCompilationJobs .
See also: AWS API Documentation
Request Syntax
client.create_compilation_job(
CompilationJobName='string',
RoleArn='string',
ModelPackageVersionArn='string',
InputConfig={
'S3Uri': 'string',
'DataInputConfig': 'string',
'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'|'TFLITE'|'DARKNET'|'SKLEARN',
'FrameworkVersion': 'string'
},
OutputConfig={
'S3OutputLocation': 'string',
'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_m6g'|'ml_c4'|'ml_c5'|'ml_c6g'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'ml_inf2'|'ml_trn1'|'ml_eia2'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'rasp4b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv2'|'amba_cv22'|'amba_cv25'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm'|'imx8mplus',
'TargetPlatform': {
'Os': 'ANDROID'|'LINUX',
'Arch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
'Accelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'|'NNA'
},
'CompilerOptions': 'string',
'KmsKeyId': 'string'
},
VpcConfig={
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
StoppingCondition={
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.
string
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
During model compilation, Amazon SageMaker needs your permission to:
Read input data from an S3 bucket
Write model artifacts to an S3 bucket
Write logs to Amazon CloudWatch Logs
Publish metrics to Amazon CloudWatch
You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.
string
The Amazon Resource Name (ARN) of a versioned model package. Provide either a ModelPackageVersionArn or an InputConfig object in the request syntax. The presence of both objects in the CreateCompilationJob request will return an exception.
dict
Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
S3Uri (string) -- [REQUIRED]
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
DataInputConfig (string) --
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are Framework specific.
TensorFlow : You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input":[1,1024,1024,3]}
If using the CLI, {\"input\":[1,1024,1024,3]}
Examples for two inputs:
If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
KERAS : You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input_1":[1,3,224,224]}
If using the CLI, {\"input_1\":[1,3,224,224]}
Examples for two inputs:
If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
MXNET/ONNX/DARKNET : You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"data":[1,3,1024,1024]}
If using the CLI, {\"data\":[1,3,1024,1024]}
Examples for two inputs:
If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
PyTorch : You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.
Examples for one input in dictionary format:
If using the console, {"input0":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224]}
Example for one input in list format: [[1,3,224,224]]
Examples for two inputs in dictionary format:
If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
XGBOOST : input data name and shape are not needed.
DataInputConfig supports the following parameters for CoreML TargetDevice (ML Model format):
shape : Input shape, for example {"input_1": {"shape": [1,224,224,3]}} . In addition to static input shapes, CoreML converter supports Flexible input shapes:
Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}
Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example: {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
default_shape : Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
type : Input type. Allowed values: Image and Tensor . By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such as bias and scale .
bias : If the input type is an Image, you need to provide the bias vector.
scale : If the input type is an Image, you need to provide a scale factor.
CoreML ClassifierConfig parameters can be specified using OutputConfig CompilerOptions . CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:
Tensor type input:
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
Tensor type input without input name (PyTorch):
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
Image type input:
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
Image type input without input name (PyTorch):
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
Depending on the model format, DataInputConfig requires the following parameters for ml_eia2 OutputConfig:TargetDevice .
For TensorFlow models saved in the SavedModel format, specify the input names from signature_def_key and the input model shapes for DataInputConfig . Specify the signature_def_key in ` OutputConfig:CompilerOptions https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html#sagemaker-Type-OutputConfig-CompilerOptions`__ if the model does not use TensorFlow's default signature def key. For example:
"DataInputConfig": {"inputs": [1, 224, 224, 3]}
"CompilerOptions": {"signature_def_key": "serving_custom"}
For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in DataInputConfig and the output tensor names for output_names in ` OutputConfig:CompilerOptions https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html#sagemaker-Type-OutputConfig-CompilerOptions`__ . For example:
"DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
"CompilerOptions": {"output_names": ["output_tensor:0"]}
Framework (string) -- [REQUIRED]
Identifies the framework in which the model was trained. For example: TENSORFLOW.
FrameworkVersion (string) --
Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.
For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks .
dict
[REQUIRED]
Provides information about the output location for the compiled model and the target device the model runs on.
S3OutputLocation (string) -- [REQUIRED]
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
TargetDevice (string) --
Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform .
Note
Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.
TargetPlatform (dict) --
Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice .
The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
Raspberry Pi 3 Model B+ "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"}, "CompilerOptions": {'mattr': ['+neon']}
Jetson TX2 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
EC2 m5.2xlarge instance OS "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'mcpu': 'skylake-avx512'}
RK3399 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
ARMv7 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"}, "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
ARMv8 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"}, "CompilerOptions": {'ANDROID_PLATFORM': 29}
Os (string) -- [REQUIRED]
Specifies a target platform OS.
LINUX : Linux-based operating systems.
ANDROID : Android operating systems. Android API level can be specified using the ANDROID_PLATFORM compiler option. For example, "CompilerOptions": {'ANDROID_PLATFORM': 28}
Arch (string) -- [REQUIRED]
Specifies a target platform architecture.
X86_64 : 64-bit version of the x86 instruction set.
X86 : 32-bit version of the x86 instruction set.
ARM64 : ARMv8 64-bit CPU.
ARM_EABIHF : ARMv7 32-bit, Hard Float.
ARM_EABI : ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.
Accelerator (string) --
Specifies a target platform accelerator (optional).
NVIDIA : Nvidia graphics processing unit. It also requires gpu-code , trt-ver , cuda-ver compiler options
MALI : ARM Mali graphics processor
INTEL_GRAPHICS : Integrated Intel graphics
CompilerOptions (string) --
Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.
DTYPE : Specifies the data type for the input. When compiling for ml_* (except for ml_inf ) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:
float32: Use either "float" or "float32" .
int64: Use either "int64" or "long" .
For example, {"dtype" : "float32"} .
CPU : Compilation for CPU supports the following compiler options.
mcpu : CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
mattr : CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
ARM : Details of ARM CPU compilations.
NEON : NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors. For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
NVIDIA : Compilation for NVIDIA GPU supports the following compiler options.
gpu_code : Specifies the targeted architecture.
trt-ver : Specifies the TensorRT versions in x.y.z. format.
cuda-ver : Specifies the CUDA version in x.y format.
For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
ANDROID : Compilation for the Android OS supports the following compiler options:
ANDROID_PLATFORM : Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28} .
mattr : Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.
INFERENTIA : Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"" . For information about supported compiler options, see Neuron Compiler CLI Reference Guide .
CoreML : Compilation for the CoreML OutputConfig TargetDevice supports the following compiler options:
class_labels : Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"} . Labels inside the txt file should be separated by newlines.
EIA : Compilation for the Elastic Inference Accelerator supports the following compiler options:
precision_mode : Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32" . Default is "FP32" .
signature_def_key : Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.
output_names : Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names .
For example: {"precision_mode": "FP32", "output_names": ["output:0"]}
KmsKeyId (string) --
The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
dict
A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) -- [REQUIRED]
The VPC security group IDs. IDs have the form of sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) -- [REQUIRED]
The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.
(string) --
dict
[REQUIRED]
Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
list
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'CompilationJobArn': 'string'
}
Response Structure
(dict) --
CompilationJobArn (string) --
If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:
CompilationJobArn : The Amazon Resource Name (ARN) of the compiled job.
{'DataCaptureConfig': {'CaptureOptions': {'CaptureMode': {'InputAndOutput'}}},
'ProductionVariants': {'InstanceType': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}},
'ShadowProductionVariants': {'InstanceType': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}}
Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API.
Note
Use this API if you want to use SageMaker hosting services to deploy models into production.
In the request, you define a ProductionVariant , for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy.
If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.
Note
When you call CreateEndpoint , a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting ` Eventually Consistent Reads https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html`__ , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.
See also: AWS API Documentation
Request Syntax
client.create_endpoint_config(
EndpointConfigName='string',
ProductionVariants=[
{
'VariantName': 'string',
'ModelName': 'string',
'InitialInstanceCount': 123,
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
'InitialVariantWeight': ...,
'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
'CoreDumpConfig': {
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
},
'ServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'VolumeSizeInGB': 123,
'ModelDataDownloadTimeoutInSeconds': 123,
'ContainerStartupHealthCheckTimeoutInSeconds': 123,
'EnableSSMAccess': True|False,
'ManagedInstanceScaling': {
'Status': 'ENABLED'|'DISABLED',
'MinInstanceCount': 123,
'MaxInstanceCount': 123
},
'RoutingConfig': {
'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
}
},
],
DataCaptureConfig={
'EnableCapture': True|False,
'InitialSamplingPercentage': 123,
'DestinationS3Uri': 'string',
'KmsKeyId': 'string',
'CaptureOptions': [
{
'CaptureMode': 'Input'|'Output'|'InputAndOutput'
},
],
'CaptureContentTypeHeader': {
'CsvContentTypes': [
'string',
],
'JsonContentTypes': [
'string',
]
}
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
KmsKeyId='string',
AsyncInferenceConfig={
'ClientConfig': {
'MaxConcurrentInvocationsPerInstance': 123
},
'OutputConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'NotificationConfig': {
'SuccessTopic': 'string',
'ErrorTopic': 'string',
'IncludeInferenceResponseIn': [
'SUCCESS_NOTIFICATION_TOPIC'|'ERROR_NOTIFICATION_TOPIC',
]
},
'S3FailurePath': 'string'
}
},
ExplainerConfig={
'ClarifyExplainerConfig': {
'EnableExplanations': 'string',
'InferenceConfig': {
'FeaturesAttribute': 'string',
'ContentTemplate': 'string',
'MaxRecordCount': 123,
'MaxPayloadInMB': 123,
'ProbabilityIndex': 123,
'LabelIndex': 123,
'ProbabilityAttribute': 'string',
'LabelAttribute': 'string',
'LabelHeaders': [
'string',
],
'FeatureHeaders': [
'string',
],
'FeatureTypes': [
'numerical'|'categorical'|'text',
]
},
'ShapConfig': {
'ShapBaselineConfig': {
'MimeType': 'string',
'ShapBaseline': 'string',
'ShapBaselineUri': 'string'
},
'NumberOfSamples': 123,
'UseLogit': True|False,
'Seed': 123,
'TextConfig': {
'Language': 'af'|'sq'|'ar'|'hy'|'eu'|'bn'|'bg'|'ca'|'zh'|'hr'|'cs'|'da'|'nl'|'en'|'et'|'fi'|'fr'|'de'|'el'|'gu'|'he'|'hi'|'hu'|'is'|'id'|'ga'|'it'|'kn'|'ky'|'lv'|'lt'|'lb'|'mk'|'ml'|'mr'|'ne'|'nb'|'fa'|'pl'|'pt'|'ro'|'ru'|'sa'|'sr'|'tn'|'si'|'sk'|'sl'|'es'|'sv'|'tl'|'ta'|'tt'|'te'|'tr'|'uk'|'ur'|'yo'|'lij'|'xx',
'Granularity': 'token'|'sentence'|'paragraph'
}
}
}
},
ShadowProductionVariants=[
{
'VariantName': 'string',
'ModelName': 'string',
'InitialInstanceCount': 123,
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
'InitialVariantWeight': ...,
'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
'CoreDumpConfig': {
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
},
'ServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'VolumeSizeInGB': 123,
'ModelDataDownloadTimeoutInSeconds': 123,
'ContainerStartupHealthCheckTimeoutInSeconds': 123,
'EnableSSMAccess': True|False,
'ManagedInstanceScaling': {
'Status': 'ENABLED'|'DISABLED',
'MinInstanceCount': 123,
'MaxInstanceCount': 123
},
'RoutingConfig': {
'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
}
},
],
ExecutionRoleArn='string',
VpcConfig={
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
EnableNetworkIsolation=True|False
)
string
[REQUIRED]
The name of the endpoint configuration. You specify this name in a CreateEndpoint request.
list
[REQUIRED]
An array of ProductionVariant objects, one for each model that you want to host at this endpoint.
(dict) --
Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants .
VariantName (string) -- [REQUIRED]
The name of the production variant.
ModelName (string) --
The name of the model that you want to host. This is the name that you specified when creating the model.
InitialInstanceCount (integer) --
Number of instances to launch initially.
InstanceType (string) --
The ML compute instance type.
InitialVariantWeight (float) --
Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.
AcceleratorType (string) --
The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .
CoreDumpConfig (dict) --
Specifies configuration for a core dump from the model container when the process crashes.
DestinationS3Uri (string) -- [REQUIRED]
The Amazon S3 bucket to send the core dump to.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
ServerlessConfig (dict) --
The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
MemorySizeInMB (integer) -- [REQUIRED]
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) -- [REQUIRED]
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.
ModelDataDownloadTimeoutInSeconds (integer) --
The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.
ContainerStartupHealthCheckTimeoutInSeconds (integer) --
The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .
EnableSSMAccess (boolean) --
You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint .
ManagedInstanceScaling (dict) --
Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status (string) --
Indicates whether managed instance scaling is enabled.
MinInstanceCount (integer) --
The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount (integer) --
The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig (dict) --
Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy (string) -- [REQUIRED]
Sets how the endpoint routes incoming traffic:
LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.
RANDOM : The endpoint routes each request to a randomly chosen instance.
dict
Configuration to control how SageMaker captures inference data.
EnableCapture (boolean) --
Whether data capture should be enabled or disabled (defaults to enabled).
InitialSamplingPercentage (integer) -- [REQUIRED]
The percentage of requests SageMaker will capture. A lower value is recommended for Endpoints with high traffic.
DestinationS3Uri (string) -- [REQUIRED]
The Amazon S3 location used to capture the data.
KmsKeyId (string) --
The Amazon Resource Name (ARN) of an Key Management Service key that SageMaker uses to encrypt the captured data at rest using Amazon S3 server-side encryption.
The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
CaptureOptions (list) -- [REQUIRED]
Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both
(dict) --
Specifies data Model Monitor will capture.
CaptureMode (string) -- [REQUIRED]
Specify the boundary of data to capture.
CaptureContentTypeHeader (dict) --
Configuration specifying how to treat different headers. If no headers are specified SageMaker will by default base64 encode when capturing the data.
CsvContentTypes (list) --
The list of all content type headers that Amazon SageMaker will treat as CSV and capture accordingly.
(string) --
JsonContentTypes (list) --
The list of all content type headers that SageMaker will treat as JSON and capture accordingly.
(string) --
list
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
string
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.
The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint , UpdateEndpoint requests. For more information, refer to the Amazon Web Services Key Management Service section`Using Key Policies in Amazon Web Services KMS <https://docs.aws.amazon.com/kms/latest/developerguide/key-policies.html>`__
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a KmsKeyId when using an instance type with local storage. If any of the models that you specify in the ProductionVariants parameter use nitro-based instances with local storage, do not specify a value for the KmsKeyId parameter. If you specify a value for KmsKeyId when using any nitro-based instances with local storage, the call to CreateEndpointConfig fails.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
dict
Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using InvokeEndpointAsync .
ClientConfig (dict) --
Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.
MaxConcurrentInvocationsPerInstance (integer) --
The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.
OutputConfig (dict) -- [REQUIRED]
Specifies the configuration for asynchronous inference invocation outputs.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
S3OutputPath (string) --
The Amazon S3 location to upload inference responses to.
NotificationConfig (dict) --
Specifies the configuration for notifications of inference results for asynchronous inference.
SuccessTopic (string) --
Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.
ErrorTopic (string) --
Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.
IncludeInferenceResponseIn (list) --
The Amazon SNS topics where you want the inference response to be included.
Note
The inference response is included only if the response size is less than or equal to 128 KB.
(string) --
S3FailurePath (string) --
The Amazon S3 location to upload failure inference responses to.
dict
A member of CreateEndpointConfig that enables explainers.
ClarifyExplainerConfig (dict) --
A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.
EnableExplanations (string) --
A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See ` EnableExplanations https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-enable`__ for additional information.
InferenceConfig (dict) --
The inference configuration parameter for the model container.
FeaturesAttribute (string) --
Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures' , it extracts a list of features [1,2,3] from request data '{"myfeatures":[1,2,3]}' .
ContentTemplate (string) --
A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features [1,2,3] into the record string '{"myfeatures":[1,2,3]}' . Required only when the model container input is in JSON Lines format.
MaxRecordCount (integer) --
The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset . A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1 , the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.
MaxPayloadInMB (integer) --
The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.
ProbabilityIndex (integer) --
A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.
Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6' , set ProbabilityIndex to 1 to select the probability value 0.6 .
Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3] .
LabelIndex (integer) --
A zero-based index used to extract a label header or list of label headers from model container output in CSV format.
Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set LabelIndex to 0 to select the label headers ['cat','dog','fish'] .
ProbabilityAttribute (string) --
A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.
Example : If the model container output of a single request is '{"predicted_label":1,"probability":0.6}' , then set ProbabilityAttribute to 'probability' .
LabelAttribute (string) --
A JMESPath expression used to locate the list of label headers in the model container output.
Example : If the model container output of a batch request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}' , then set LabelAttribute to 'labels' to extract the list of label headers ["cat","dog","fish"]
LabelHeaders (list) --
For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.
(string) --
FeatureHeaders (list) --
The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.
(string) --
FeatureTypes (list) --
A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, ['text'] ). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.
(string) --
ShapConfig (dict) -- [REQUIRED]
The configuration for SHAP analysis.
ShapBaselineConfig (dict) -- [REQUIRED]
The configuration for the SHAP baseline of the Kernal SHAP algorithm.
MimeType (string) --
The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines' . Defaults to 'text/csv' .
ShapBaseline (string) --
The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.
ShapBaselineUri (string) --
The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud .
NumberOfSamples (integer) --
The number of samples to be used for analysis by the Kernal SHAP algorithm.
Note
The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint .
UseLogit (boolean) --
A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.
Seed (integer) --
The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.
TextConfig (dict) --
A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.
Language (string) -- [REQUIRED]
Specifies the language of the text features in `ISO 639-1 < https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes>`__ or ISO 639-3 code of a supported language.
Note
For a mix of multiple languages, use code 'xx' .
Granularity (string) -- [REQUIRED]
The unit of granularity for the analysis of text features. For example, if the unit is 'token' , then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.
list
An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants . If you use this field, you can only specify one variant for ProductionVariants and one variant for ShadowProductionVariants .
(dict) --
Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants .
VariantName (string) -- [REQUIRED]
The name of the production variant.
ModelName (string) --
The name of the model that you want to host. This is the name that you specified when creating the model.
InitialInstanceCount (integer) --
Number of instances to launch initially.
InstanceType (string) --
The ML compute instance type.
InitialVariantWeight (float) --
Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.
AcceleratorType (string) --
The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .
CoreDumpConfig (dict) --
Specifies configuration for a core dump from the model container when the process crashes.
DestinationS3Uri (string) -- [REQUIRED]
The Amazon S3 bucket to send the core dump to.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
ServerlessConfig (dict) --
The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
MemorySizeInMB (integer) -- [REQUIRED]
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) -- [REQUIRED]
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.
ModelDataDownloadTimeoutInSeconds (integer) --
The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.
ContainerStartupHealthCheckTimeoutInSeconds (integer) --
The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .
EnableSSMAccess (boolean) --
You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint .
ManagedInstanceScaling (dict) --
Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status (string) --
Indicates whether managed instance scaling is enabled.
MinInstanceCount (integer) --
The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount (integer) --
The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig (dict) --
Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy (string) -- [REQUIRED]
Sets how the endpoint routes incoming traffic:
LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.
RANDOM : The endpoint routes each request to a randomly chosen instance.
string
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform actions on your behalf. For more information, see SageMaker Roles .
Note
To be able to pass this role to Amazon SageMaker, the caller of this action must have the iam:PassRole permission.
dict
Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .
SecurityGroupIds (list) -- [REQUIRED]
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) -- [REQUIRED]
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
boolean
Sets whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.
dict
Response Syntax
{
'EndpointConfigArn': 'string'
}
Response Structure
(dict) --
EndpointConfigArn (string) --
The Amazon Resource Name (ARN) of the endpoint configuration.
{'OfflineStoreConfig': {'TableFormat': {'Default'}}}
Create a new FeatureGroup . A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record .
The FeatureGroup defines the schema and features contained in the FeatureGroup . A FeatureGroup definition is composed of a list of Features , a RecordIdentifierFeatureName , an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore . Check Amazon Web Services service quotas to see the FeatureGroup s quota for your Amazon Web Services account.
Note that it can take approximately 10-15 minutes to provision an OnlineStore FeatureGroup with the InMemory StorageType .
Warning
You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup .
See also: AWS API Documentation
Request Syntax
client.create_feature_group(
FeatureGroupName='string',
RecordIdentifierFeatureName='string',
EventTimeFeatureName='string',
FeatureDefinitions=[
{
'FeatureName': 'string',
'FeatureType': 'Integral'|'Fractional'|'String',
'CollectionType': 'List'|'Set'|'Vector',
'CollectionConfig': {
'VectorConfig': {
'Dimension': 123
}
}
},
],
OnlineStoreConfig={
'SecurityConfig': {
'KmsKeyId': 'string'
},
'EnableOnlineStore': True|False,
'TtlDuration': {
'Unit': 'Seconds'|'Minutes'|'Hours'|'Days'|'Weeks',
'Value': 123
},
'StorageType': 'Standard'|'InMemory'
},
OfflineStoreConfig={
'S3StorageConfig': {
'S3Uri': 'string',
'KmsKeyId': 'string',
'ResolvedOutputS3Uri': 'string'
},
'DisableGlueTableCreation': True|False,
'DataCatalogConfig': {
'TableName': 'string',
'Catalog': 'string',
'Database': 'string'
},
'TableFormat': 'Default'|'Glue'|'Iceberg'
},
RoleArn='string',
Description='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the FeatureGroup . The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. The name:
Must start and end with an alphanumeric character.
Can only contain alphanumeric character and hyphens. Spaces are not allowed.
string
[REQUIRED]
The name of the Feature whose value uniquely identifies a Record defined in the FeatureStore . Only the latest record per identifier value will be stored in the OnlineStore . RecordIdentifierFeatureName must be one of feature definitions' names.
You use the RecordIdentifierFeatureName to access data in a FeatureStore .
This name:
Must start and end with an alphanumeric character.
Can only contains alphanumeric characters, hyphens, underscores. Spaces are not allowed.
string
[REQUIRED]
The name of the feature that stores the EventTime of a Record in a FeatureGroup .
An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup . All Records in the FeatureGroup must have a corresponding EventTime .
An EventTime can be a String or Fractional .
Fractional : EventTime feature values must be a Unix timestamp in seconds.
String : EventTime feature values must be an ISO-8601 string in the format. The following formats are supported yyyy-MM-dd'T'HH:mm:ssZ and yyyy-MM-dd'T'HH:mm:ss.SSSZ where yyyy , MM , and dd represent the year, month, and day respectively and HH , mm , ss , and if applicable, SSS represent the hour, month, second and milliseconds respsectively. 'T' and Z are constants.
list
[REQUIRED]
A list of Feature names and types. Name and Type is compulsory per Feature .
Valid feature FeatureType s are Integral , Fractional and String .
FeatureName s cannot be any of the following: is_deleted , write_time , api_invocation_time
You can create up to 2,500 FeatureDefinition s per FeatureGroup .
(dict) --
A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .
FeatureName (string) -- [REQUIRED]
The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .
FeatureType (string) -- [REQUIRED]
The value type of a feature. Valid values are Integral, Fractional, or String.
CollectionType (string) --
A grouping of elements where each element within the collection must have the same feature type (String , Integral , or Fractional ).
List : An ordered collection of elements.
Set : An unordered collection of unique elements.
Vector : A specialized list that represents a fixed-size array of elements. The vector dimension is determined by you. Must have elements with fractional feature types.
CollectionConfig (dict) --
Configuration for your collection.
VectorConfig (dict) --
Configuration for your vector collection type.
Dimension : The number of elements in your vector.
Dimension (integer) -- [REQUIRED]
The number of elements in your vector.
dict
You can turn the OnlineStore on or off by specifying True for the EnableOnlineStore flag in OnlineStoreConfig .
You can also include an Amazon Web Services KMS key ID (KMSKeyId ) for at-rest encryption of the OnlineStore .
The default value is False .
SecurityConfig (dict) --
Use to specify KMS Key ID (KMSKeyId ) for at-rest encryption of your OnlineStore .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key ARN that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId :
"kms:Encrypt"
"kms:Decrypt"
"kms:DescribeKey"
"kms:CreateGrant"
"kms:RetireGrant"
"kms:ReEncryptFrom"
"kms:ReEncryptTo"
"kms:GenerateDataKey"
"kms:ListAliases"
"kms:ListGrants"
"kms:RevokeGrant"
The caller (either user or IAM role) to all DataPlane operations (PutRecord , GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId :
"kms:Decrypt"
EnableOnlineStore (boolean) --
Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.
The default value is False .
TtlDuration (dict) --
Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration . For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.
Unit (string) --
TtlDuration time unit.
Value (integer) --
TtlDuration time value.
StorageType (string) --
Option for different tiers of low latency storage for real-time data retrieval.
Standard : A managed low latency data store for feature groups.
InMemory : A managed data store for feature groups that supports very low latency retrieval.
dict
Use this to configure an OfflineFeatureStore . This parameter allows you to specify:
The Amazon Simple Storage Service (Amazon S3) location of an OfflineStore .
A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data catalog.
An KMS encryption key to encrypt the Amazon S3 location used for OfflineStore . If KMS encryption key is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your bucket-level key for SSE, you can reduce Amazon Web Services KMS requests costs by up to 99 percent.
Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg .
To learn more about this parameter, see OfflineStoreConfig .
S3StorageConfig (dict) -- [REQUIRED]
The Amazon Simple Storage (Amazon S3) location of OfflineStore .
S3Uri (string) -- [REQUIRED]
The S3 URI, or location in Amazon S3, of OfflineStore .
S3 URIs have a format similar to the following: s3://example-bucket/prefix/ .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key ARN of the key used to encrypt any objects written into the OfflineStore S3 location.
The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId :
"kms:GenerateDataKey"
ResolvedOutputS3Uri (string) --
The S3 path where offline records are written.
DisableGlueTableCreation (boolean) --
Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore . If set to False , Feature Store will name the OfflineStore Glue table following Athena's naming recommendations .
The default value is False .
DataCatalogConfig (dict) --
The meta data of the Glue table that is autogenerated when an OfflineStore is created.
TableName (string) -- [REQUIRED]
The name of the Glue table.
Catalog (string) -- [REQUIRED]
The name of the Glue table catalog.
Database (string) -- [REQUIRED]
The name of the Glue table database.
TableFormat (string) --
Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg .
string
The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
string
A free-form description of a FeatureGroup .
list
Tags used to identify Features in each FeatureGroup .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'FeatureGroupArn': 'string'
}
Response Structure
(dict) --
FeatureGroupArn (string) --
The Amazon Resource Name (ARN) of the FeatureGroup . This is a unique identifier for the feature group.
{'HumanLoopRequestSource': {'AwsManagedHumanLoopRequestSource': {'AWS/Bedrock/ModelEvaluation',
'AWS/Handshake/VerifyIdentity',
'AWS/Textract/AnalyzeExpense'}}}
Creates a flow definition.
See also: AWS API Documentation
Request Syntax
client.create_flow_definition(
FlowDefinitionName='string',
HumanLoopRequestSource={
'AwsManagedHumanLoopRequestSource': 'AWS/Rekognition/DetectModerationLabels/Image/V3'|'AWS/Textract/AnalyzeDocument/Forms/V1'|'AWS/Textract/AnalyzeExpense'|'AWS/Handshake/VerifyIdentity'|'AWS/Bedrock/ModelEvaluation'
},
HumanLoopActivationConfig={
'HumanLoopActivationConditionsConfig': {
'HumanLoopActivationConditions': 'string'
}
},
HumanLoopConfig={
'WorkteamArn': 'string',
'HumanTaskUiArn': 'string',
'TaskTitle': 'string',
'TaskDescription': 'string',
'TaskCount': 123,
'TaskAvailabilityLifetimeInSeconds': 123,
'TaskTimeLimitInSeconds': 123,
'TaskKeywords': [
'string',
],
'PublicWorkforceTaskPrice': {
'AmountInUsd': {
'Dollars': 123,
'Cents': 123,
'TenthFractionsOfACent': 123
}
}
},
OutputConfig={
'S3OutputPath': 'string',
'KmsKeyId': 'string'
},
RoleArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of your flow definition.
dict
Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
AwsManagedHumanLoopRequestSource (string) -- [REQUIRED]
Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:
dict
An object containing information about the events that trigger a human workflow.
HumanLoopActivationConditionsConfig (dict) -- [REQUIRED]
Container structure for defining under what conditions SageMaker creates a human loop.
HumanLoopActivationConditions (string) -- [REQUIRED]
JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide .
dict
An object containing information about the tasks the human reviewers will perform.
WorkteamArn (string) -- [REQUIRED]
Amazon Resource Name (ARN) of a team of workers. To learn more about the types of workforces and work teams you can create and use with Amazon A2I, see Create and Manage Workforces .
HumanTaskUiArn (string) -- [REQUIRED]
The Amazon Resource Name (ARN) of the human task user interface.
You can use standard HTML and Crowd HTML Elements to create a custom worker task template. You use this template to create a human task UI.
To learn how to create a custom HTML template, see Create Custom Worker Task Template .
To learn how to create a human task UI, which is a worker task template that can be used in a flow definition, see Create and Delete a Worker Task Templates .
TaskTitle (string) -- [REQUIRED]
A title for the human worker task.
TaskDescription (string) -- [REQUIRED]
A description for the human worker task.
TaskCount (integer) -- [REQUIRED]
The number of distinct workers who will perform the same task on each object. For example, if TaskCount is set to 3 for an image classification labeling job, three workers will classify each input image. Increasing TaskCount can improve label accuracy.
TaskAvailabilityLifetimeInSeconds (integer) --
The length of time that a task remains available for review by human workers.
TaskTimeLimitInSeconds (integer) --
The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).
TaskKeywords (list) --
Keywords used to describe the task so that workers can discover the task.
(string) --
PublicWorkforceTaskPrice (dict) --
Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.
Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer.
0.036
0.048
0.060
0.072
0.120
0.240
0.360
0.480
0.600
0.720
0.840
0.960
1.080
1.200
Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.
0.012
0.024
0.036
0.048
0.060
0.072
0.120
0.240
0.360
0.480
0.600
0.720
0.840
0.960
1.080
1.200
Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.
0.840
0.960
1.080
1.200
Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.
2.400
2.280
2.160
2.040
1.920
1.800
1.680
1.560
1.440
1.320
1.200
1.080
0.960
0.840
0.720
0.600
0.480
0.360
0.240
0.120
0.072
0.060
0.048
0.036
0.024
0.012
Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars.
1.200
1.080
0.960
0.840
0.720
0.600
0.480
0.360
0.240
0.120
0.072
0.060
0.048
0.036
0.024
0.012
Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars.
1.200
1.080
0.960
0.840
0.720
0.600
0.480
0.360
0.240
0.120
0.072
0.060
0.048
0.036
0.024
0.012
AmountInUsd (dict) --
Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
Dollars (integer) --
The whole number of dollars in the amount.
Cents (integer) --
The fractional portion, in cents, of the amount.
TenthFractionsOfACent (integer) --
Fractions of a cent, in tenths.
dict
[REQUIRED]
An object containing information about where the human review results will be uploaded.
S3OutputPath (string) -- [REQUIRED]
The Amazon S3 path where the object containing human output will be made available.
To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data .
KmsKeyId (string) --
The Amazon Key Management Service (KMS) key ID for server-side encryption.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298 .
list
An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'FlowDefinitionArn': 'string'
}
Response Structure
(dict) --
FlowDefinitionArn (string) --
The Amazon Resource Name (ARN) of the flow definition you create.
{'TrainingJobDefinition': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}}},
'TrainingJobDefinitions': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}}}}
Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components .
Warning
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
See also: AWS API Documentation
Request Syntax
client.create_hyper_parameter_tuning_job(
HyperParameterTuningJobName='string',
HyperParameterTuningJobConfig={
'Strategy': 'Bayesian'|'Random'|'Hyperband'|'Grid',
'StrategyConfig': {
'HyperbandStrategyConfig': {
'MinResource': 123,
'MaxResource': 123
}
},
'HyperParameterTuningJobObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'ResourceLimits': {
'MaxNumberOfTrainingJobs': 123,
'MaxParallelTrainingJobs': 123,
'MaxRuntimeInSeconds': 123
},
'ParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'TrainingJobEarlyStoppingType': 'Off'|'Auto',
'TuningJobCompletionCriteria': {
'TargetObjectiveMetricValue': ...,
'BestObjectiveNotImproving': {
'MaxNumberOfTrainingJobsNotImproving': 123
},
'ConvergenceDetected': {
'CompleteOnConvergence': 'Disabled'|'Enabled'
}
},
'RandomSeed': 123
},
TrainingJobDefinition={
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'AlgorithmName': 'string',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'KeepAlivePeriodInSeconds': 123,
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
]
},
'HyperParameterTuningResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'AllocationStrategy': 'Prioritized',
'InstanceConfigs': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123
},
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
},
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'Environment': {
'string': 'string'
}
},
TrainingJobDefinitions=[
{
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'AlgorithmName': 'string',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'KeepAlivePeriodInSeconds': 123,
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
]
},
'HyperParameterTuningResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'AllocationStrategy': 'Prioritized',
'InstanceConfigs': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123
},
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
},
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'Environment': {
'string': 'string'
}
},
],
WarmStartConfig={
'ParentHyperParameterTuningJobs': [
{
'HyperParameterTuningJobName': 'string'
},
],
'WarmStartType': 'IdenticalDataAndAlgorithm'|'TransferLearning'
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
Autotune={
'Mode': 'Enabled'
}
)
string
[REQUIRED]
The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
dict
[REQUIRED]
The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works .
Strategy (string) -- [REQUIRED]
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works .
StrategyConfig (dict) --
The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig .
HyperbandStrategyConfig (dict) --
The configuration for the object that specifies the Hyperband strategy. This parameter is only supported for the Hyperband selection for Strategy within the HyperParameterTuningJobConfig API.
MinResource (integer) --
The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job is not stopped by Hyperband .
MaxResource (integer) --
The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTrainingJ attempts to infer MaxResource from the following keys (if present) in StaticsHyperParameters :
epochs
numepochs
n-epochs
n_epochs
num_epochs
If HyperbandStrategyConfig is unable to infer a value for MaxResource , it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions . For distributive training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.
HyperParameterTuningJobObjective (dict) --
The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
Type (string) -- [REQUIRED]
Whether to minimize or maximize the objective metric.
MetricName (string) -- [REQUIRED]
The name of the metric to use for the objective metric.
ResourceLimits (dict) -- [REQUIRED]
The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
MaxNumberOfTrainingJobs (integer) --
The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs (integer) -- [REQUIRED]
The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
MaxRuntimeInSeconds (integer) --
The maximum time in seconds that a hyperparameter tuning job can run.
ParameterRanges (dict) --
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) -- [REQUIRED]
The name of the hyperparameter to search.
MinValue (string) -- [REQUIRED]
The minimum value of the hyperparameter to search.
MaxValue (string) -- [REQUIRED]
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) -- [REQUIRED]
The name of the continuous hyperparameter to tune.
MinValue (string) -- [REQUIRED]
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) -- [REQUIRED]
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) -- [REQUIRED]
The name of the categorical hyperparameter to tune.
Values (list) -- [REQUIRED]
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) -- [REQUIRED]
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) -- [REQUIRED]
An example value of the hyperparameter to optimize using Autotune.
TrainingJobEarlyStoppingType (string) --
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband . This parameter can take on one of the following values (the default value is OFF ):
OFF
Training jobs launched by the hyperparameter tuning job do not use early stopping.
AUTO
SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early .
TuningJobCompletionCriteria (dict) --
The tuning job's completion criteria.
TargetObjectiveMetricValue (float) --
The value of the objective metric.
BestObjectiveNotImproving (dict) --
A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.
MaxNumberOfTrainingJobsNotImproving (integer) --
The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
ConvergenceDetected (dict) --
A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.
CompleteOnConvergence (string) --
A flag to stop a tuning job once AMT has detected that the job has converged.
RandomSeed (integer) --
A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.
dict
The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
DefinitionName (string) --
The job definition name.
TuningObjective (dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables .
Type (string) -- [REQUIRED]
Whether to minimize or maximize the objective metric.
MetricName (string) -- [REQUIRED]
The name of the metric to use for the objective metric.
HyperParameterRanges (dict) --
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) -- [REQUIRED]
The name of the hyperparameter to search.
MinValue (string) -- [REQUIRED]
The minimum value of the hyperparameter to search.
MaxValue (string) -- [REQUIRED]
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) -- [REQUIRED]
The name of the continuous hyperparameter to tune.
MinValue (string) -- [REQUIRED]
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) -- [REQUIRED]
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) -- [REQUIRED]
The name of the categorical hyperparameter to tune.
Values (list) -- [REQUIRED]
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) -- [REQUIRED]
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) -- [REQUIRED]
An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters (dict) --
Specifies the values of hyperparameters that do not change for the tuning job.
(string) --
(string) --
AlgorithmSpecification (dict) -- [REQUIRED]
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
TrainingInputMode (string) -- [REQUIRED]
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
AlgorithmName (string) --
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
MetricDefinitions (list) --
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) -- [REQUIRED]
The name of the metric.
Regex (string) -- [REQUIRED]
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
RoleArn (string) -- [REQUIRED]
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig (list) --
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) -- [REQUIRED]
The name of the channel.
DataSource (dict) -- [REQUIRED]
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) -- [REQUIRED]
The file system id.
FileSystemAccessMode (string) -- [REQUIRED]
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) -- [REQUIRED]
The file system type.
DirectoryPath (string) -- [REQUIRED]
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) -- [REQUIRED]
Determines the shuffling order in ShuffleConfig value.
VpcConfig (dict) --
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) -- [REQUIRED]
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) -- [REQUIRED]
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
OutputDataConfig (dict) -- [REQUIRED]
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
Note
If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) -- [REQUIRED]
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) -- [REQUIRED]
Specifies the instance type of the instance group.
InstanceCount (integer) -- [REQUIRED]
Specifies the number of instances of the instance group.
InstanceGroupName (string) -- [REQUIRED]
Specifies the name of the instance group.
HyperParameterTuningResourceConfig (dict) --
The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
InstanceType (string) --
The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
InstanceCount (integer) --
The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.
VolumeSizeInGB (integer) --
The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.
Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .
Note
SageMaker supports only the General Purpose SSD (gp2) storage volume type.
VolumeKmsKeyId (string) --
A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
AllocationStrategy (string) --
The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
InstanceConfigs (list) --
A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.
Note
If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .
(dict) --
The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
InstanceType (string) -- [REQUIRED]
The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .
InstanceCount (integer) -- [REQUIRED]
The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
VolumeSizeInGB (integer) -- [REQUIRED]
The volume size in GB of the data to be processed for hyperparameter optimization (optional).
StoppingCondition (dict) -- [REQUIRED]
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
EnableNetworkIsolation (boolean) --
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) -- [REQUIRED]
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Environment (dict) --
An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note
The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
(string) --
(string) --
list
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
(dict) --
Defines the training jobs launched by a hyperparameter tuning job.
DefinitionName (string) --
The job definition name.
TuningObjective (dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables .
Type (string) -- [REQUIRED]
Whether to minimize or maximize the objective metric.
MetricName (string) -- [REQUIRED]
The name of the metric to use for the objective metric.
HyperParameterRanges (dict) --
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) -- [REQUIRED]
The name of the hyperparameter to search.
MinValue (string) -- [REQUIRED]
The minimum value of the hyperparameter to search.
MaxValue (string) -- [REQUIRED]
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) -- [REQUIRED]
The name of the continuous hyperparameter to tune.
MinValue (string) -- [REQUIRED]
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) -- [REQUIRED]
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) -- [REQUIRED]
The name of the categorical hyperparameter to tune.
Values (list) -- [REQUIRED]
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) -- [REQUIRED]
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) -- [REQUIRED]
An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters (dict) --
Specifies the values of hyperparameters that do not change for the tuning job.
(string) --
(string) --
AlgorithmSpecification (dict) -- [REQUIRED]
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
TrainingInputMode (string) -- [REQUIRED]
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
AlgorithmName (string) --
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
MetricDefinitions (list) --
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) -- [REQUIRED]
The name of the metric.
Regex (string) -- [REQUIRED]
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
RoleArn (string) -- [REQUIRED]
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig (list) --
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) -- [REQUIRED]
The name of the channel.
DataSource (dict) -- [REQUIRED]
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) -- [REQUIRED]
The file system id.
FileSystemAccessMode (string) -- [REQUIRED]
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) -- [REQUIRED]
The file system type.
DirectoryPath (string) -- [REQUIRED]
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) -- [REQUIRED]
Determines the shuffling order in ShuffleConfig value.
VpcConfig (dict) --
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) -- [REQUIRED]
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) -- [REQUIRED]
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
OutputDataConfig (dict) -- [REQUIRED]
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
Note
If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) -- [REQUIRED]
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) -- [REQUIRED]
Specifies the instance type of the instance group.
InstanceCount (integer) -- [REQUIRED]
Specifies the number of instances of the instance group.
InstanceGroupName (string) -- [REQUIRED]
Specifies the name of the instance group.
HyperParameterTuningResourceConfig (dict) --
The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
InstanceType (string) --
The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
InstanceCount (integer) --
The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.
VolumeSizeInGB (integer) --
The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.
Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .
Note
SageMaker supports only the General Purpose SSD (gp2) storage volume type.
VolumeKmsKeyId (string) --
A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
AllocationStrategy (string) --
The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
InstanceConfigs (list) --
A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.
Note
If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .
(dict) --
The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
InstanceType (string) -- [REQUIRED]
The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .
InstanceCount (integer) -- [REQUIRED]
The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
VolumeSizeInGB (integer) -- [REQUIRED]
The volume size in GB of the data to be processed for hyperparameter optimization (optional).
StoppingCondition (dict) -- [REQUIRED]
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
EnableNetworkIsolation (boolean) --
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) -- [REQUIRED]
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Environment (dict) --
An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note
The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
(string) --
(string) --
dict
Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.
Note
All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.
ParentHyperParameterTuningJobs (list) -- [REQUIRED]
An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point .
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
(dict) --
A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
HyperParameterTuningJobName (string) --
The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
WarmStartType (string) -- [REQUIRED]
Specifies one of the following:
IDENTICAL_DATA_AND_ALGORITHM
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
TRANSFER_LEARNING
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
list
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:
ParameterRanges : The names and ranges of parameters that a hyperparameter tuning job can optimize.
ResourceLimits : The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.
TrainingJobEarlyStoppingType : A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.
RetryStrategy : The number of times to retry a training job.
Strategy : Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.
ConvergenceDetected : A flag to indicate that Automatic model tuning (AMT) has detected model convergence.
Mode (string) -- [REQUIRED]
Set Mode to Enabled if you want to use Autotune.
dict
Response Syntax
{
'HyperParameterTuningJobArn': 'string'
}
Response Structure
(dict) --
HyperParameterTuningJobArn (string) --
The Amazon Resource Name (ARN) of the tuning job. SageMaker assigns an ARN to a hyperparameter tuning job when you create it.
{'InputConfig': {'EndpointConfigurations': {'InstanceType': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}}}
Starts a recommendation job. You can create either an instance recommendation or load test job.
See also: AWS API Documentation
Request Syntax
client.create_inference_recommendations_job(
JobName='string',
JobType='Default'|'Advanced',
RoleArn='string',
InputConfig={
'ModelPackageVersionArn': 'string',
'ModelName': 'string',
'JobDurationInSeconds': 123,
'TrafficPattern': {
'TrafficType': 'PHASES'|'STAIRS',
'Phases': [
{
'InitialNumberOfUsers': 123,
'SpawnRate': 123,
'DurationInSeconds': 123
},
],
'Stairs': {
'DurationInSeconds': 123,
'NumberOfSteps': 123,
'UsersPerStep': 123
}
},
'ResourceLimit': {
'MaxNumberOfTests': 123,
'MaxParallelOfTests': 123
},
'EndpointConfigurations': [
{
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
'ServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'InferenceSpecificationName': 'string',
'EnvironmentParameterRanges': {
'CategoricalParameterRanges': [
{
'Name': 'string',
'Value': [
'string',
]
},
]
}
},
],
'VolumeKmsKeyId': 'string',
'ContainerConfig': {
'Domain': 'string',
'Task': 'string',
'Framework': 'string',
'FrameworkVersion': 'string',
'PayloadConfig': {
'SamplePayloadUrl': 'string',
'SupportedContentTypes': [
'string',
]
},
'NearestModelName': 'string',
'SupportedInstanceTypes': [
'string',
],
'SupportedEndpointType': 'RealTime'|'Serverless',
'DataInputConfig': 'string',
'SupportedResponseMIMETypes': [
'string',
]
},
'Endpoints': [
{
'EndpointName': 'string'
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
JobDescription='string',
StoppingConditions={
'MaxInvocations': 123,
'ModelLatencyThresholds': [
{
'Percentile': 'string',
'ValueInMilliseconds': 123
},
],
'FlatInvocations': 'Continue'|'Stop'
},
OutputConfig={
'KmsKeyId': 'string',
'CompiledOutputConfig': {
'S3OutputUri': 'string'
}
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
A name for the recommendation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account. The job name is passed down to the resources created by the recommendation job. The names of resources (such as the model, endpoint configuration, endpoint, and compilation) that are prefixed with the job name are truncated at 40 characters.
string
[REQUIRED]
Defines the type of recommendation job. Specify Default to initiate an instance recommendation and Advanced to initiate a load test. If left unspecified, Amazon SageMaker Inference Recommender will run an instance recommendation (DEFAULT ) job.
string
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
dict
[REQUIRED]
Provides information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations.
ModelPackageVersionArn (string) --
The Amazon Resource Name (ARN) of a versioned model package.
ModelName (string) --
The name of the created model.
JobDurationInSeconds (integer) --
Specifies the maximum duration of the job, in seconds. The maximum value is 18,000 seconds.
TrafficPattern (dict) --
Specifies the traffic pattern of the job.
TrafficType (string) --
Defines the traffic patterns. Choose either PHASES or STAIRS .
Phases (list) --
Defines the phases traffic specification.
(dict) --
Defines the traffic pattern.
InitialNumberOfUsers (integer) --
Specifies how many concurrent users to start with. The value should be between 1 and 3.
SpawnRate (integer) --
Specified how many new users to spawn in a minute.
DurationInSeconds (integer) --
Specifies how long a traffic phase should be. For custom load tests, the value should be between 120 and 3600. This value should not exceed JobDurationInSeconds .
Stairs (dict) --
Defines the stairs traffic pattern.
DurationInSeconds (integer) --
Defines how long each traffic step should be.
NumberOfSteps (integer) --
Specifies how many steps to perform during traffic.
UsersPerStep (integer) --
Specifies how many new users to spawn in each step.
ResourceLimit (dict) --
Defines the resource limit of the job.
MaxNumberOfTests (integer) --
Defines the maximum number of load tests.
MaxParallelOfTests (integer) --
Defines the maximum number of parallel load tests.
EndpointConfigurations (list) --
Specifies the endpoint configuration to use for a job.
(dict) --
The endpoint configuration for the load test.
InstanceType (string) --
The instance types to use for the load test.
ServerlessConfig (dict) --
Specifies the serverless configuration for an endpoint variant.
MemorySizeInMB (integer) -- [REQUIRED]
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) -- [REQUIRED]
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
InferenceSpecificationName (string) --
The inference specification name in the model package version.
EnvironmentParameterRanges (dict) --
The parameter you want to benchmark against.
CategoricalParameterRanges (list) --
Specified a list of parameters for each category.
(dict) --
Environment parameters you want to benchmark your load test against.
Name (string) -- [REQUIRED]
The Name of the environment variable.
Value (list) -- [REQUIRED]
The list of values you can pass.
(string) --
VolumeKmsKeyId (string) --
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. This key will be passed to SageMaker Hosting for endpoint creation.
The SageMaker execution role must have kms:CreateGrant permission in order to encrypt data on the storage volume of the endpoints created for inference recommendation. The inference recommendation job will fail asynchronously during endpoint configuration creation if the role passed does not have kms:CreateGrant permission.
The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"
For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
ContainerConfig (dict) --
Specifies mandatory fields for running an Inference Recommender job. The fields specified in ContainerConfig override the corresponding fields in the model package.
Domain (string) --
The machine learning domain of the model and its components.
Valid Values: COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING
Task (string) --
The machine learning task that the model accomplishes.
Valid Values: IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER
Framework (string) --
The machine learning framework of the container image.
Valid Values: TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN
FrameworkVersion (string) --
The framework version of the container image.
PayloadConfig (dict) --
Specifies the SamplePayloadUrl and all other sample payload-related fields.
SamplePayloadUrl (string) --
The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
NearestModelName (string) --
The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model.
Valid Values: efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet
SupportedInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
(string) --
SupportedEndpointType (string) --
The endpoint type to receive recommendations for. By default this is null, and the results of the inference recommendation job return a combined list of both real-time and serverless benchmarks. By specifying a value for this field, you can receive a longer list of benchmarks for the desired endpoint type.
DataInputConfig (string) --
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. This field is used for optimizing your model using SageMaker Neo. For more information, see DataInputConfig .
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
Endpoints (list) --
Existing customer endpoints on which to run an Inference Recommender job.
(dict) --
Details about a customer endpoint that was compared in an Inference Recommender job.
EndpointName (string) --
The name of a customer's endpoint.
VpcConfig (dict) --
Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.
SecurityGroupIds (list) -- [REQUIRED]
The VPC security group IDs. IDs have the form of sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) -- [REQUIRED]
The ID of the subnets in the VPC to which you want to connect your model.
(string) --
string
Description of the recommendation job.
dict
A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped.
MaxInvocations (integer) --
The maximum number of requests per minute expected for the endpoint.
ModelLatencyThresholds (list) --
The interval of time taken by a model to respond as viewed from SageMaker. The interval includes the local communication time taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container.
(dict) --
The model latency threshold.
Percentile (string) --
The model latency percentile threshold. Acceptable values are P95 and P99 . For custom load tests, specify the value as P95 .
ValueInMilliseconds (integer) --
The model latency percentile value in milliseconds.
FlatInvocations (string) --
Stops a load test when the number of invocations (TPS) peaks and flattens, which means that the instance has reached capacity. The default value is Stop . If you want the load test to continue after invocations have flattened, set the value to Continue .
dict
Provides information about the output artifacts and the KMS key to use for Amazon S3 server-side encryption.
KmsKeyId (string) --
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt your output artifacts with Amazon S3 server-side encryption. The SageMaker execution role must have kms:GenerateDataKey permission.
The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"
For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
CompiledOutputConfig (dict) --
Provides information about the output configuration for the compiled model.
S3OutputUri (string) --
Identifies the Amazon S3 bucket where you want SageMaker to store the compiled model artifacts.
list
The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see Tagging Amazon Web Services Resources in the Amazon Web Services General Reference.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'JobArn': 'string'
}
Response Structure
(dict) --
JobArn (string) --
The Amazon Resource Name (ARN) of the recommendation job.
{'AdditionalInferenceSpecifications': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}},
'InferenceSpecification': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}}
Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification . To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification .
Note
There are two types of model packages:
Versioned - a model that is part of a model group in the model registry.
Unversioned - a model package that is not part of a model group.
See also: AWS API Documentation
Request Syntax
client.create_model_package(
ModelPackageName='string',
ModelPackageGroupName='string',
ModelPackageDescription='string',
InferenceSpecification={
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalS3DataSource': {
'S3DataType': 'S3Object',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
ValidationSpecification={
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
SourceAlgorithmSpecification={
'SourceAlgorithms': [
{
'ModelDataUrl': 'string',
'AlgorithmName': 'string'
},
]
},
CertifyForMarketplace=True|False,
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval',
MetadataProperties={
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
ModelMetrics={
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Bias': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PreTrainingReport': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PostTrainingReport': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
ClientToken='string',
Domain='string',
Task='string',
SamplePayloadUrl='string',
CustomerMetadataProperties={
'string': 'string'
},
DriftCheckBaselines={
'Bias': {
'ConfigFile': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PreTrainingConstraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PostTrainingConstraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'ConfigFile': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
AdditionalInferenceSpecifications=[
{
'Name': 'string',
'Description': 'string',
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalS3DataSource': {
'S3DataType': 'S3Object',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
],
SkipModelValidation='All'|'None'
)
string
The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
This parameter is required for unversioned models. It is not applicable to versioned models.
string
The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.
This parameter is required for versioned models, and does not apply to unversioned models.
string
A description of the model package.
dict
Specifies details about inference jobs that can be run with models based on this model package, including the following:
The Amazon ECR paths of containers that contain the inference code and model artifacts.
The instance types that the model package supports for transform jobs and real-time endpoints used for inference.
The input and output content formats that the model package supports for inference.
Containers (list) -- [REQUIRED]
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) -- [REQUIRED]
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) -- [REQUIRED]
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) -- [REQUIRED]
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) -- [REQUIRED]
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
dict
Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.
ValidationRole (string) -- [REQUIRED]
The IAM roles to be used for the validation of the model package.
ValidationProfiles (list) -- [REQUIRED]
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.
(dict) --
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
ProfileName (string) -- [REQUIRED]
The name of the profile for the model package.
TransformJobDefinition (dict) -- [REQUIRED]
The TransformJobDefinition object that describes the transform job used for the validation of the model package.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) -- [REQUIRED]
A description of the input source and the way the transform job consumes it.
DataSource (dict) -- [REQUIRED]
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) -- [REQUIRED]
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) -- [REQUIRED]
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) -- [REQUIRED]
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) -- [REQUIRED]
Identifies the ML compute instances for the transform job.
InstanceType (string) -- [REQUIRED]
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) -- [REQUIRED]
The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
dict
Details about the algorithm that was used to create the model package.
SourceAlgorithms (list) -- [REQUIRED]
A list of the algorithms that were used to create a model package.
(dict) --
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same Amazon Web Services region as the algorithm.
AlgorithmName (string) -- [REQUIRED]
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
boolean
Whether to certify the model package for listing on Amazon Web Services Marketplace.
This parameter is optional for unversioned models, and does not apply to versioned models.
list
A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
If you supply ModelPackageGroupName , your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a tag argument.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
string
Whether the model is approved for deployment.
This parameter is optional for versioned models, and does not apply to unversioned models.
For versioned models, the value of this parameter must be set to Approved to deploy the model.
dict
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
dict
A structure that contains model metrics reports.
ModelQuality (dict) --
Metrics that measure the quality of a model.
Statistics (dict) --
Model quality statistics.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Constraints (dict) --
Model quality constraints.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
ModelDataQuality (dict) --
Metrics that measure the quality of the input data for a model.
Statistics (dict) --
Data quality statistics for a model.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Constraints (dict) --
Data quality constraints for a model.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Bias (dict) --
Metrics that measure bais in a model.
Report (dict) --
The bias report for a model
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
PreTrainingReport (dict) --
The pre-training bias report for a model.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
PostTrainingReport (dict) --
The post-training bias report for a model.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Explainability (dict) --
Metrics that help explain a model.
Report (dict) --
The explainability report for a model.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
string
A unique token that guarantees that the call to this API is idempotent.
This field is autopopulated if not provided.
string
The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
string
The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" |"IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER" .
Specify "OTHER" if none of the tasks listed fit your use case.
string
The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.
dict
The metadata properties associated with the model package versions.
(string) --
(string) --
dict
Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide .
Bias (dict) --
Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
ConfigFile (dict) --
The bias config file for a model.
ContentType (string) --
The type of content stored in the file source.
ContentDigest (string) --
The digest of the file source.
S3Uri (string) -- [REQUIRED]
The Amazon S3 URI for the file source.
PreTrainingConstraints (dict) --
The pre-training constraints.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
PostTrainingConstraints (dict) --
The post-training constraints.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Explainability (dict) --
Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
Constraints (dict) --
The drift check explainability constraints.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
ConfigFile (dict) --
The explainability config file for the model.
ContentType (string) --
The type of content stored in the file source.
ContentDigest (string) --
The digest of the file source.
S3Uri (string) -- [REQUIRED]
The Amazon S3 URI for the file source.
ModelQuality (dict) --
Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
Statistics (dict) --
The drift check model quality statistics.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Constraints (dict) --
The drift check model quality constraints.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
ModelDataQuality (dict) --
Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.
Statistics (dict) --
The drift check model data quality statistics.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
Constraints (dict) --
The drift check model data quality constraints.
ContentType (string) -- [REQUIRED]
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) -- [REQUIRED]
The S3 URI for the metrics source.
list
An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
(dict) --
A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package
Name (string) -- [REQUIRED]
A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
Description (string) --
A description of the additional Inference specification
Containers (list) -- [REQUIRED]
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) -- [REQUIRED]
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) -- [REQUIRED]
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) -- [REQUIRED]
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) -- [REQUIRED]
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
string
Indicates if you want to skip model validation.
dict
Response Syntax
{
'ModelPackageArn': 'string'
}
Response Structure
(dict) --
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the new model package.
{'SpaceSettings': {'AppType': {'Canvas',
'DatasetManager',
'DetailedProfiler',
'Local',
'RSession',
'SageMakerLite',
'Savitur',
'VSCode'}}}
Creates a space used for real time collaboration in a Domain.
See also: AWS API Documentation
Request Syntax
client.create_space(
DomainId='string',
SpaceName='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
SpaceSettings={
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'CodeEditorAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
}
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
},
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
'SpaceStorageSettings': {
'EbsStorageSettings': {
'EbsVolumeSizeInGb': 123
}
},
'CustomFileSystems': [
{
'EFSFileSystem': {
'FileSystemId': 'string'
}
},
]
},
OwnershipSettings={
'OwnerUserProfileName': 'string'
},
SpaceSharingSettings={
'SharingType': 'Private'|'Shared'
},
SpaceDisplayName='string'
)
string
[REQUIRED]
The ID of the associated Domain.
string
[REQUIRED]
The name of the space.
list
Tags to associated with the space. Each tag consists of a key and an optional value. Tag keys must be unique for each resource. Tags are searchable using the Search API.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
A collection of space settings.
JupyterServerAppSettings (dict) --
The JupyterServer app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The KernelGateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image .
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
AppType (string) --
The type of app created within the space.
SpaceStorageSettings (dict) --
The storage settings for a private space.
EbsStorageSettings (dict) --
A collection of EBS storage settings for a private space.
EbsVolumeSizeInGb (integer) -- [REQUIRED]
The size of an EBS storage volume for a private space.
CustomFileSystems (list) --
A file system, created by you, that you assign to a space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
(dict) --
A file system, created by you, that you assign to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystem (dict) --
A custom file system in Amazon EFS.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
dict
A collection of ownership settings.
OwnerUserProfileName (string) -- [REQUIRED]
The user profile who is the owner of the private space.
dict
A collection of space sharing settings.
SharingType (string) -- [REQUIRED]
Specifies the sharing type of the space.
string
The name of the space that appears in the SageMaker Studio UI.
dict
Response Syntax
{
'SpaceArn': 'string'
}
Response Structure
(dict) --
SpaceArn (string) --
The space's Amazon Resource Name (ARN).
{'StudioLifecycleConfigAppType': {'Savitur', 'VSCode'}}
Creates a new Amazon SageMaker Studio Lifecycle Configuration.
See also: AWS API Documentation
Request Syntax
client.create_studio_lifecycle_config(
StudioLifecycleConfigName='string',
StudioLifecycleConfigContent='string',
StudioLifecycleConfigAppType='JupyterServer'|'KernelGateway'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
string
[REQUIRED]
The name of the Amazon SageMaker Studio Lifecycle Configuration to create.
string
[REQUIRED]
The content of your Amazon SageMaker Studio Lifecycle Configuration script. This content must be base64 encoded.
string
[REQUIRED]
The App type that the Lifecycle Configuration is attached to.
list
Tags to be associated with the Lifecycle Configuration. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
dict
Response Syntax
{
'StudioLifecycleConfigArn': 'string'
}
Response Structure
(dict) --
StudioLifecycleConfigArn (string) --
The ARN of your created Lifecycle Configuration.
{'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}}}
Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.
If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.
In the request body, you provide the following:
AlgorithmSpecification - Identifies the training algorithm to use.
HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms .
Warning
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored.
OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training.
ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.
EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training .
RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training.
StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.
Environment - The environment variables to set in the Docker container.
RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError .
For more information about SageMaker, see How It Works .
See also: AWS API Documentation
Request Syntax
client.create_training_job(
TrainingJobName='string',
HyperParameters={
'string': 'string'
},
AlgorithmSpecification={
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False,
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'TrainingImageConfig': {
'TrainingRepositoryAccessMode': 'Platform'|'Vpc',
'TrainingRepositoryAuthConfig': {
'TrainingRepositoryCredentialsProviderArn': 'string'
}
}
},
RoleArn='string',
InputDataConfig=[
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
OutputDataConfig={
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
ResourceConfig={
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'KeepAlivePeriodInSeconds': 123,
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
]
},
VpcConfig={
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
StoppingCondition={
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
EnableNetworkIsolation=True|False,
EnableInterContainerTrafficEncryption=True|False,
EnableManagedSpotTraining=True|False,
CheckpointConfig={
'S3Uri': 'string',
'LocalPath': 'string'
},
DebugHookConfig={
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
DebugRuleConfigurations=[
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
TensorBoardOutputConfig={
'LocalPath': 'string',
'S3OutputPath': 'string'
},
ExperimentConfig={
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
},
ProfilerConfig={
'S3OutputPath': 'string',
'ProfilingIntervalInMilliseconds': 123,
'ProfilingParameters': {
'string': 'string'
},
'DisableProfiler': True|False
},
ProfilerRuleConfigurations=[
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
Environment={
'string': 'string'
},
RetryStrategy={
'MaximumRetryAttempts': 123
},
InfraCheckConfig={
'EnableInfraCheck': True|False
}
)
string
[REQUIRED]
The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.
dict
Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms .
You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint .
Warning
Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.
(string) --
(string) --
dict
[REQUIRED]
The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by SageMaker, see Algorithms . For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker .
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker .
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
For more information, see the note in the AlgorithmName parameter description.
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage , and vice versa.
If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.
TrainingInputMode (string) -- [REQUIRED]
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) -- [REQUIRED]
The name of the metric.
Regex (string) -- [REQUIRED]
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
You use one of the SageMaker built-in algorithms
You use one of the following Prebuilt SageMaker Docker Images :
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
ContainerEntrypoint (list) --
The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
(string) --
TrainingImageConfig (dict) --
The configuration to use an image from a private Docker registry for a training job.
TrainingRepositoryAccessMode (string) -- [REQUIRED]
The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to Vpc .
TrainingRepositoryAuthConfig (dict) --
An object containing authentication information for a private Docker registry containing your training images.
TrainingRepositoryCredentialsProviderArn (string) -- [REQUIRED]
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
string
[REQUIRED]
The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.
During model training, SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see SageMaker Roles .
Note
To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.
list
An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.
Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data . The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.
Depending on the input mode that the algorithm supports, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded.
Your input must be in the same Amazon Web Services region as your training job.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) -- [REQUIRED]
The name of the channel.
DataSource (dict) -- [REQUIRED]
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) -- [REQUIRED]
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) -- [REQUIRED]
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) -- [REQUIRED]
The file system id.
FileSystemAccessMode (string) -- [REQUIRED]
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) -- [REQUIRED]
The file system type.
DirectoryPath (string) -- [REQUIRED]
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) -- [REQUIRED]
Determines the shuffling order in ShuffleConfig value.
dict
[REQUIRED]
Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
dict
[REQUIRED]
The resources, including the ML compute instances and ML storage volumes, to use for model training.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) -- [REQUIRED]
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) -- [REQUIRED]
Specifies the instance type of the instance group.
InstanceCount (integer) -- [REQUIRED]
Specifies the number of instances of the instance group.
InstanceGroupName (string) -- [REQUIRED]
Specifies the name of the instance group.
dict
A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) -- [REQUIRED]
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) -- [REQUIRED]
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
dict
[REQUIRED]
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
list
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) -- [REQUIRED]
The tag key. Tag keys must be unique per resource.
Value (string) -- [REQUIRED]
The tag value.
boolean
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
boolean
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job .
boolean
To train models using managed spot training, choose True . Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.
The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.
dict
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) -- [REQUIRED]
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
dict
Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
LocalPath (string) --
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) -- [REQUIRED]
Path to Amazon S3 storage location for metrics and tensors.
HookParameters (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters.
(string) --
(string) --
CollectionConfigurations (list) --
Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(dict) --
Configuration information for the Amazon SageMaker Debugger output tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
(string) --
(string) --
list
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
(dict) --
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
RuleConfigurationName (string) -- [REQUIRED]
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) -- [REQUIRED]
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a custom rule for debugging a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
dict
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) -- [REQUIRED]
Path to Amazon S3 storage location for TensorBoard output.
dict
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
dict
Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath (string) --
Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds (integer) --
A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters (dict) --
Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig , PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(string) --
(string) --
DisableProfiler (boolean) --
Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to True .
list
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
(dict) --
Configuration information for profiling rules.
RuleConfigurationName (string) -- [REQUIRED]
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) -- [REQUIRED]
The Amazon Elastic Container Registry Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a custom rule for profiling a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
dict
The environment variables to set in the Docker container.
(string) --
(string) --
dict
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) -- [REQUIRED]
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
dict
Contains information about the infrastructure health check configuration for the training job.
EnableInfraCheck (boolean) --
Enables an infrastructure health check.
dict
Response Syntax
{
'TrainingJobArn': 'string'
}
Response Structure
(dict) --
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
{'AppType': {'Canvas',
'DatasetManager',
'DetailedProfiler',
'Local',
'RSession',
'SageMakerLite',
'Savitur',
'VSCode'}}
Used to stop and delete an app.
See also: AWS API Documentation
Request Syntax
client.delete_app(
DomainId='string',
UserProfileName='string',
SpaceName='string',
AppType='JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
AppName='string'
)
string
[REQUIRED]
The domain ID.
string
The user profile name. If this value is not set, then SpaceName must be set.
string
The name of the space. If this value is not set, then UserProfileName must be set.
string
[REQUIRED]
The type of app.
string
[REQUIRED]
The name of the app.
None
{'InferenceSpecification': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}},
'TrainingSpecification': {'SupportedTrainingInstanceTypes': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'ValidationSpecification': {'ValidationProfiles': {'TrainingJobDefinition': {'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}}}}}}
Returns a description of the specified algorithm that is in your account.
See also: AWS API Documentation
Request Syntax
client.describe_algorithm(
AlgorithmName='string'
)
string
[REQUIRED]
The name of the algorithm to describe.
dict
Response Syntax
{
'AlgorithmName': 'string',
'AlgorithmArn': 'string',
'AlgorithmDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingSpecification': {
'TrainingImage': 'string',
'TrainingImageDigest': 'string',
'SupportedHyperParameters': [
{
'Name': 'string',
'Description': 'string',
'Type': 'Integer'|'Continuous'|'Categorical'|'FreeText',
'Range': {
'IntegerParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'ContinuousParameterRangeSpecification': {
'MinValue': 'string',
'MaxValue': 'string'
},
'CategoricalParameterRangeSpecification': {
'Values': [
'string',
]
}
},
'IsTunable': True|False,
'IsRequired': True|False,
'DefaultValue': 'string'
},
],
'SupportedTrainingInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
],
'SupportsDistributedTraining': True|False,
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'TrainingChannels': [
{
'Name': 'string',
'Description': 'string',
'IsRequired': True|False,
'SupportedContentTypes': [
'string',
],
'SupportedCompressionTypes': [
'None'|'Gzip',
],
'SupportedInputModes': [
'Pipe'|'File'|'FastFile',
]
},
],
'SupportedTuningJobObjectiveMetrics': [
{
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
],
'AdditionalS3DataSource': {
'S3DataType': 'S3Object',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip'
}
},
'InferenceSpecification': {
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalS3DataSource': {
'S3DataType': 'S3Object',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
'ValidationSpecification': {
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TrainingJobDefinition': {
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'HyperParameters': {
'string': 'string'
},
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'KeepAlivePeriodInSeconds': 123,
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
}
},
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
'AlgorithmStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'AlgorithmStatusDetails': {
'ValidationStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
],
'ImageScanStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
]
},
'ProductId': 'string',
'CertifyForMarketplace': True|False
}
Response Structure
(dict) --
AlgorithmName (string) --
The name of the algorithm being described.
AlgorithmArn (string) --
The Amazon Resource Name (ARN) of the algorithm.
AlgorithmDescription (string) --
A brief summary about the algorithm.
CreationTime (datetime) --
A timestamp specifying when the algorithm was created.
TrainingSpecification (dict) --
Details about training jobs run by this algorithm.
TrainingImage (string) --
The Amazon ECR registry path of the Docker image that contains the training algorithm.
TrainingImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
SupportedHyperParameters (list) --
A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>
(dict) --
Defines a hyperparameter to be used by an algorithm.
Name (string) --
The name of this hyperparameter. The name must be unique.
Description (string) --
A brief description of the hyperparameter.
Type (string) --
The type of this hyperparameter. The valid types are Integer , Continuous , Categorical , and FreeText .
Range (dict) --
The allowed range for this hyperparameter.
IntegerParameterRangeSpecification (dict) --
A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.
MinValue (string) --
The minimum integer value allowed.
MaxValue (string) --
The maximum integer value allowed.
ContinuousParameterRangeSpecification (dict) --
A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.
MinValue (string) --
The minimum floating-point value allowed.
MaxValue (string) --
The maximum floating-point value allowed.
CategoricalParameterRangeSpecification (dict) --
A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.
Values (list) --
The allowed categories for the hyperparameter.
(string) --
IsTunable (boolean) --
Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
IsRequired (boolean) --
Indicates whether this hyperparameter is required.
DefaultValue (string) --
The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
SupportedTrainingInstanceTypes (list) --
A list of the instance types that this algorithm can use for training.
(string) --
SupportsDistributedTraining (boolean) --
Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
MetricDefinitions (list) --
A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
TrainingChannels (list) --
A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.
(dict) --
Defines a named input source, called a channel, to be used by an algorithm.
Name (string) --
The name of the channel.
Description (string) --
A brief description of the channel.
IsRequired (boolean) --
Indicates whether the channel is required by the algorithm.
SupportedContentTypes (list) --
The supported MIME types for the data.
(string) --
SupportedCompressionTypes (list) --
The allowed compression types, if data compression is used.
(string) --
SupportedInputModes (list) --
The allowed input mode, either FILE or PIPE.
In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode.
In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
(string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
SupportedTuningJobObjectiveMetrics (list) --
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
(dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables .
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
AdditionalS3DataSource (dict) --
The additional data source used during the training job.
S3DataType (string) --
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) --
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
InferenceSpecification (dict) --
Details about inference jobs that the algorithm runs.
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) --
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) --
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) --
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
ValidationSpecification (dict) --
Details about configurations for one or more training jobs that SageMaker runs to test the algorithm.
ValidationRole (string) --
The IAM roles that SageMaker uses to run the training jobs.
ValidationProfiles (list) --
An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.
(dict) --
Defines a training job and a batch transform job that SageMaker runs to validate your algorithm.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
ProfileName (string) --
The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
TrainingJobDefinition (dict) --
The TrainingJobDefinition object that describes the training job that SageMaker runs to validate your algorithm.
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
HyperParameters (dict) --
The hyperparameters used for the training job.
(string) --
(string) --
InputDataConfig (list) --
An array of Channel objects, each of which specifies an input source.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the ML compute instances and ML storage volumes, to use for model training.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
TransformJobDefinition (dict) --
The TransformJobDefinition object that describes the transform job that SageMaker runs to validate your algorithm.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
A description of the input source and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) --
Identifies the ML compute instances for the transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
AlgorithmStatus (string) --
The current status of the algorithm.
AlgorithmStatusDetails (dict) --
Details about the current status of the algorithm.
ValidationStatuses (list) --
The status of algorithm validation.
(dict) --
Represents the overall status of an algorithm.
Name (string) --
The name of the algorithm for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
ImageScanStatuses (list) --
The status of the scan of the algorithm's Docker image container.
(dict) --
Represents the overall status of an algorithm.
Name (string) --
The name of the algorithm for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
ProductId (string) --
The product identifier of the algorithm.
CertifyForMarketplace (boolean) --
Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.
{'AppType': {'Canvas',
'DatasetManager',
'DetailedProfiler',
'Local',
'RSession',
'SageMakerLite',
'Savitur',
'VSCode'}}
Describes the app.
See also: AWS API Documentation
Request Syntax
client.describe_app(
DomainId='string',
UserProfileName='string',
SpaceName='string',
AppType='JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
AppName='string'
)
string
[REQUIRED]
The domain ID.
string
The user profile name. If this value is not set, then SpaceName must be set.
string
The name of the space.
string
[REQUIRED]
The type of app.
string
[REQUIRED]
The name of the app.
dict
Response Syntax
{
'AppArn': 'string',
'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
'AppName': 'string',
'DomainId': 'string',
'UserProfileName': 'string',
'SpaceName': 'string',
'Status': 'Deleted'|'Deleting'|'Failed'|'InService'|'Pending',
'LastHealthCheckTimestamp': datetime(2015, 1, 1),
'LastUserActivityTimestamp': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'ResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
}
}
Response Structure
(dict) --
AppArn (string) --
The Amazon Resource Name (ARN) of the app.
AppType (string) --
The type of app.
AppName (string) --
The name of the app.
DomainId (string) --
The domain ID.
UserProfileName (string) --
The user profile name.
SpaceName (string) --
The name of the space. If this value is not set, then UserProfileName must be set.
Status (string) --
The status.
LastHealthCheckTimestamp (datetime) --
The timestamp of the last health check.
LastUserActivityTimestamp (datetime) --
The timestamp of the last user's activity. LastUserActivityTimestamp is also updated when SageMaker performs health checks without user activity. As a result, this value is set to the same value as LastHealthCheckTimestamp .
CreationTime (datetime) --
The creation time.
FailureReason (string) --
The failure reason.
ResourceSpec (dict) --
The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
{'JupyterLabAppImageConfig': {'FileSystemConfig': {'DefaultGid': 'integer',
'DefaultUid': 'integer',
'MountPath': 'string'}}}
Describes an AppImageConfig.
See also: AWS API Documentation
Request Syntax
client.describe_app_image_config(
AppImageConfigName='string'
)
string
[REQUIRED]
The name of the AppImageConfig to describe.
dict
Response Syntax
{
'AppImageConfigArn': 'string',
'AppImageConfigName': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'KernelGatewayImageConfig': {
'KernelSpecs': [
{
'Name': 'string',
'DisplayName': 'string'
},
],
'FileSystemConfig': {
'MountPath': 'string',
'DefaultUid': 123,
'DefaultGid': 123
}
},
'JupyterLabAppImageConfig': {
'FileSystemConfig': {
'MountPath': 'string',
'DefaultUid': 123,
'DefaultGid': 123
},
'ContainerConfig': {
'ContainerArguments': [
'string',
],
'ContainerEntrypoint': [
'string',
],
'ContainerEnvironmentVariables': {
'string': 'string'
}
}
}
}
Response Structure
(dict) --
AppImageConfigArn (string) --
The Amazon Resource Name (ARN) of the AppImageConfig.
AppImageConfigName (string) --
The name of the AppImageConfig.
CreationTime (datetime) --
When the AppImageConfig was created.
LastModifiedTime (datetime) --
When the AppImageConfig was last modified.
KernelGatewayImageConfig (dict) --
The configuration of a KernelGateway app.
KernelSpecs (list) --
The specification of the Jupyter kernels in the image.
(dict) --
The specification of a Jupyter kernel.
Name (string) --
The name of the Jupyter kernel in the image. This value is case sensitive.
DisplayName (string) --
The display name of the kernel.
FileSystemConfig (dict) --
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
MountPath (string) --
The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
DefaultUid (integer) --
The default POSIX user ID (UID). If not specified, defaults to 1000 .
DefaultGid (integer) --
The default POSIX group ID (GID). If not specified, defaults to 100 .
JupyterLabAppImageConfig (dict) --
The configuration of the JupyterLab app.
FileSystemConfig (dict) --
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
MountPath (string) --
The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
DefaultUid (integer) --
The default POSIX user ID (UID). If not specified, defaults to 1000 .
DefaultGid (integer) --
The default POSIX group ID (GID). If not specified, defaults to 100 .
ContainerConfig (dict) --
The configuration used to run the application image container.
ContainerArguments (list) --
The arguments for the container when you're running the application.
(string) --
ContainerEntrypoint (list) --
The entrypoint used to run the application in the container.
(string) --
ContainerEnvironmentVariables (dict) --
The environment variables to set in the container
(string) --
(string) --
{'AutoMLProblemTypeConfig': {'TextGenerationJobConfig': {'ModelAccessConfig': {'AcceptEula': 'boolean'}}}}
Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob .
See also: AWS API Documentation
Request Syntax
client.describe_auto_ml_job_v2(
AutoMLJobName='string'
)
string
[REQUIRED]
Requests information about an AutoML job V2 using its unique name.
dict
Response Syntax
{
'AutoMLJobName': 'string',
'AutoMLJobArn': 'string',
'AutoMLJobInputDataConfig': [
{
'ChannelType': 'training'|'validation',
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string'
},
'RoleArn': 'string',
'AutoMLJobObjective': {
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
},
'AutoMLProblemTypeConfig': {
'ImageClassificationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
}
},
'TextClassificationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'ContentColumn': 'string',
'TargetLabelColumn': 'string'
},
'TimeSeriesForecastingJobConfig': {
'FeatureSpecificationS3Uri': 'string',
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'ForecastFrequency': 'string',
'ForecastHorizon': 123,
'ForecastQuantiles': [
'string',
],
'Transformations': {
'Filling': {
'string': {
'string': 'string'
}
},
'Aggregation': {
'string': 'sum'|'avg'|'first'|'min'|'max'
}
},
'TimeSeriesConfig': {
'TargetAttributeName': 'string',
'TimestampAttributeName': 'string',
'ItemIdentifierAttributeName': 'string',
'GroupingAttributeNames': [
'string',
]
},
'HolidayConfig': [
{
'CountryCode': 'string'
},
]
},
'TabularJobConfig': {
'CandidateGenerationConfig': {
'AlgorithmsConfig': [
{
'AutoMLAlgorithms': [
'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai',
]
},
]
},
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'FeatureSpecificationS3Uri': 'string',
'Mode': 'AUTO'|'ENSEMBLING'|'HYPERPARAMETER_TUNING',
'GenerateCandidateDefinitionsOnly': True|False,
'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
'TargetAttributeName': 'string',
'SampleWeightAttributeName': 'string'
},
'TextGenerationJobConfig': {
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'BaseModelName': 'string',
'TextGenerationHyperParameters': {
'string': 'string'
},
'ModelAccessConfig': {
'AcceptEula': True|False
}
}
},
'AutoMLProblemTypeConfigName': 'ImageClassification'|'TextClassification'|'TimeSeriesForecasting'|'Tabular'|'TextGeneration',
'CreationTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'PartialFailureReasons': [
{
'PartialFailureMessage': 'string'
},
],
'BestCandidate': {
'CandidateName': 'string',
'FinalAutoMLJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss',
'Value': ...,
'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed',
'CandidateSteps': [
{
'CandidateStepType': 'AWS::SageMaker::TrainingJob'|'AWS::SageMaker::TransformJob'|'AWS::SageMaker::ProcessingJob',
'CandidateStepArn': 'string',
'CandidateStepName': 'string'
},
],
'CandidateStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'InferenceContainers': [
{
'Image': 'string',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
}
},
],
'CreationTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'CandidateProperties': {
'CandidateArtifactLocations': {
'Explainability': 'string',
'ModelInsights': 'string',
'BacktestResults': 'string'
},
'CandidateMetrics': [
{
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss',
'Value': ...,
'Set': 'Train'|'Validation'|'Test',
'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'LogLoss'|'InferenceLatency'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'|'Rouge1'|'Rouge2'|'RougeL'|'RougeLSum'|'Perplexity'|'ValidationLoss'|'TrainingLoss'
},
]
},
'InferenceContainerDefinitions': {
'string': [
{
'Image': 'string',
'ModelDataUrl': 'string',
'Environment': {
'string': 'string'
}
},
]
}
},
'AutoMLJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
'AutoMLJobSecondaryStatus': 'Starting'|'MaxCandidatesReached'|'Failed'|'Stopped'|'MaxAutoMLJobRuntimeReached'|'Stopping'|'CandidateDefinitionsGenerated'|'Completed'|'ExplainabilityError'|'DeployingModel'|'ModelDeploymentError'|'GeneratingModelInsightsReport'|'ModelInsightsError'|'AnalyzingData'|'FeatureEngineering'|'ModelTuning'|'GeneratingExplainabilityReport'|'TrainingModels'|'PreTraining',
'AutoMLJobArtifacts': {
'CandidateDefinitionNotebookLocation': 'string',
'DataExplorationNotebookLocation': 'string'
},
'ResolvedAttributes': {
'AutoMLJobObjective': {
'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
},
'CompletionCriteria': {
'MaxCandidates': 123,
'MaxRuntimePerTrainingJobInSeconds': 123,
'MaxAutoMLJobRuntimeInSeconds': 123
},
'AutoMLProblemTypeResolvedAttributes': {
'TabularResolvedAttributes': {
'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression'
},
'TextGenerationResolvedAttributes': {
'BaseModelName': 'string'
}
}
},
'ModelDeployConfig': {
'AutoGenerateEndpointName': True|False,
'EndpointName': 'string'
},
'ModelDeployResult': {
'EndpointName': 'string'
},
'DataSplitConfig': {
'ValidationFraction': ...
},
'SecurityConfig': {
'VolumeKmsKeyId': 'string',
'EnableInterContainerTrafficEncryption': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
}
}
Response Structure
(dict) --
AutoMLJobName (string) --
Returns the name of the AutoML job V2.
AutoMLJobArn (string) --
Returns the Amazon Resource Name (ARN) of the AutoML job V2.
AutoMLJobInputDataConfig (list) --
Returns an array of channel objects describing the input data and their location.
(dict) --
A channel is a named input source that training algorithms can consume. This channel is used for AutoML jobs V2 (jobs created by calling CreateAutoMLJobV2 ).
ChannelType (string) --
The type of channel. Defines whether the data are used for training or validation. The default value is training . Channels for training and validation must share the same ContentType
Note
The type of channel defaults to training for the time-series forecasting problem type.
ContentType (string) --
The content type of the data from the input source. The following are the allowed content types for different problems:
For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
For image classification: image/png , image/jpeg , or image/* . The default value is image/* .
For text classification: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet . The default value is text/csv;header=present .
CompressionType (string) --
The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None . If no compression type is provided, we default to None .
DataSource (dict) --
The data source for an AutoML channel (Required).
S3DataSource (dict) --
The Amazon S3 location of the input data.
S3DataType (string) --
The data type.
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. The S3Prefix should have the following format: s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. A ManifestFile should have the format shown below: [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"}, "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1", "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2", ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2 ). Here is a minimal, single-record example of an AugmentedManifestFile : {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg", "label-metadata": {"class-name": "cat" } For more information on AugmentedManifestFile , see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File .
S3Uri (string) --
The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.
OutputDataConfig (dict) --
Returns the job's output data config.
KmsKeyId (string) --
The Key Management Service (KMS) encryption key ID.
S3OutputPath (string) --
The Amazon S3 output path. Must be 128 characters or less.
RoleArn (string) --
The ARN of the Identity and Access Management role that has read permission to the input data location and write permission to the output data location in Amazon S3.
AutoMLJobObjective (dict) --
Returns the job's objective.
MetricName (string) --
The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
For tabular problem types:
List of available metrics:
Regression: InferenceLatency , MAE , MSE , R2 , RMSE
Binary classification: Accuracy , AUC , BalancedAccuracy , F1 , InferenceLatency , LogLoss , Precision , Recall
Multiclass classification: Accuracy , BalancedAccuracy , F1macro , InferenceLatency , LogLoss , PrecisionMacro , RecallMacro
For a description of each metric, see Autopilot metrics for classification and regression .
Default objective metrics:
Regression: MSE .
Binary classification: F1 .
Multiclass classification: Accuracy .
For image or text classification problem types:
List of available metrics: Accuracy For a description of each metric, see Autopilot metrics for text and image classification .
Default objective metrics: Accuracy
For time-series forecasting problem types:
List of available metrics: RMSE , wQL , Average wQL , MASE , MAPE , WAPE For a description of each metric, see Autopilot metrics for time-series forecasting .
Default objective metrics: AverageWeightedQuantileLoss
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot .
AutoMLProblemTypeConfig (dict) --
Returns the configuration settings of the problem type set for the AutoML job V2.
ImageClassificationJobConfig (dict) --
Settings used to configure an AutoML job V2 for the image classification problem type.
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
TextClassificationJobConfig (dict) --
Settings used to configure an AutoML job V2 for the text classification problem type.
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
ContentColumn (string) --
The name of the column used to provide the sentences to be classified. It should not be the same as the target column.
TargetLabelColumn (string) --
The name of the column used to provide the class labels. It should not be same as the content column.
TimeSeriesForecastingJobConfig (dict) --
Settings used to configure an AutoML job V2 for the time-series forecasting problem type.
FeatureSpecificationS3Uri (string) --
A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig . When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig . If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig .
You can input FeatureAttributeNames (optional) in JSON format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] } .
You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Autopilot supports the following data types: numeric , categorical , text , and datetime .
Note
These column keys must not include any column set in TimeSeriesConfig .
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
ForecastFrequency (string) --
The frequency of predictions in a forecast.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, 1D indicates every day and 15min indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min .
The valid values for each frequency are the following:
Minute - 1-59
Hour - 1-23
Day - 1-6
Week - 1-4
Month - 1-11
Year - 1
ForecastHorizon (integer) --
The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.
ForecastQuantiles (list) --
The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.
(string) --
Transformations (dict) --
The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.
Filling (dict) --
A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic. You can specify multiple filling methods for a single column.
The supported filling methods and their corresponding options are:
frontfill : none (Supported only for target column)
middlefill : zero , value , median , mean , min , max
backfill : zero , value , median , mean , min , max
futurefill : zero , value , median , mean , min , max
To set a filling method to a specific value, set the fill parameter to the chosen filling method value (for example "backfill" : "value" ), and define the filling value in an additional parameter prefixed with "_value". For example, to set backfill to a value of 2 , you must include two parameters: "backfill": "value" and "backfill_value":"2" .
(string) --
(dict) --
(string) --
(string) --
Aggregation (dict) --
A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method.
The supported aggregation methods are sum (default), avg , first , min , max .
Note
Aggregation is only supported for the target column.
(string) --
(string) --
TimeSeriesConfig (dict) --
The collection of components that defines the time-series.
TargetAttributeName (string) --
The name of the column representing the target variable that you want to predict for each item in your dataset. The data type of the target variable must be numerical.
TimestampAttributeName (string) --
The name of the column indicating a point in time at which the target value of a given item is recorded.
ItemIdentifierAttributeName (string) --
The name of the column that represents the set of item identifiers for which you want to predict the target value.
GroupingAttributeNames (list) --
A set of columns names that can be grouped with the item identifier column to create a composite key for which a target value is predicted.
(string) --
HolidayConfig (list) --
The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.
(dict) --
Stores the holiday featurization attributes applicable to each item of time-series datasets during the training of a forecasting model. This allows the model to identify patterns associated with specific holidays.
CountryCode (string) --
The country code for the holiday calendar.
For the list of public holiday calendars supported by AutoML job V2, see Country Codes . Use the country code corresponding to the country of your choice.
TabularJobConfig (dict) --
Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).
CandidateGenerationConfig (dict) --
The configuration information of how model candidates are generated.
AlgorithmsConfig (list) --
Stores the configuration information for the selection of algorithms used to train model candidates on tabular data.
The list of available algorithms to choose from depends on the training mode set in ` TabularJobConfig.Mode https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TabularJobConfig.html`__ .
AlgorithmsConfig should not be set in AUTO training mode.
When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.
For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig .
For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.
(dict) --
The collection of algorithms run on a dataset for training the model candidates of an Autopilot job.
AutoMLAlgorithms (list) --
The selection of algorithms run on a dataset to train the model candidates of an Autopilot job.
Note
Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING ). Choose a minimum of 1 algorithm.
In ENSEMBLING mode:
"catboost"
"extra-trees"
"fastai"
"lightgbm"
"linear-learner"
"nn-torch"
"randomforest"
"xgboost"
In HYPERPARAMETER_TUNING mode:
"linear-learner"
"mlp"
"xgboost"
(string) --
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
FeatureSpecificationS3Uri (string) --
A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:
{ "FeatureAttributeNames":["col1", "col2", ...] } .
You can also specify the data type of the feature (optional) in the format shown below:
{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }
Note
These column keys may not include the target column.
In ensembling mode, Autopilot only supports the following data types: numeric , categorical , text , and datetime . In HPO mode, Autopilot can support numeric , categorical , text , datetime , and sequence .
If only FeatureDataTypes is provided, the column keys (col1 , col2 ,..) should be a subset of the column names in the input data.
If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames .
The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.
Mode (string) --
The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO . In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.
The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.
The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
GenerateCandidateDefinitionsOnly (boolean) --
Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
ProblemType (string) --
The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see Amazon SageMaker Autopilot problem types .
Note
You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.
TargetAttributeName (string) --
The name of the target variable in supervised learning, usually represented by 'y'.
SampleWeightAttributeName (string) --
If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation .
Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.
Support for sample weights is available in Ensembling mode only.
TextGenerationJobConfig (dict) --
Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type.
Note
The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions .
CompletionCriteria (dict) --
How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
BaseModelName (string) --
The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot . If no BaseModelName is provided, the default model used is Falcon7BInstruct .
TextGenerationHyperParameters (dict) --
The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters .
"epochCount" : The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".
"batchSize" : The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".
"learningRate" : The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".
"learningRateWarmupSteps" : The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".
Here is an example where all four hyperparameters are configured.
{ "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" }
(string) --
(string) --
ModelAccessConfig (dict) --
The access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . For more information, see End-user license agreements .
AcceptEula (boolean) --
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AutoMLProblemTypeConfigName (string) --
Returns the name of the problem type configuration set for the AutoML job V2.
CreationTime (datetime) --
Returns the creation time of the AutoML job V2.
EndTime (datetime) --
Returns the end time of the AutoML job V2.
LastModifiedTime (datetime) --
Returns the job's last modified time.
FailureReason (string) --
Returns the reason for the failure of the AutoML job V2, when applicable.
PartialFailureReasons (list) --
Returns a list of reasons for partial failures within an AutoML job V2.
(dict) --
The reason for a partial failure of an AutoML job.
PartialFailureMessage (string) --
The message containing the reason for a partial failure of an AutoML job.
BestCandidate (dict) --
Information about the candidate produced by an AutoML training job V2, including its status, steps, and other properties.
CandidateName (string) --
The name of the candidate.
FinalAutoMLJobObjectiveMetric (dict) --
The best candidate result from an AutoML training job.
Type (string) --
The type of metric with the best result.
MetricName (string) --
The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName .
Value (float) --
The value of the metric with the best result.
StandardMetricName (string) --
The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics .
ObjectiveStatus (string) --
The objective's status.
CandidateSteps (list) --
Information about the candidate's steps.
(dict) --
Information about the steps for a candidate and what step it is working on.
CandidateStepType (string) --
Whether the candidate is at the transform, training, or processing step.
CandidateStepArn (string) --
The ARN for the candidate's step.
CandidateStepName (string) --
The name for the candidate's step.
CandidateStatus (string) --
The candidate's status.
InferenceContainers (list) --
Information about the recommended inference container definitions.
(dict) --
A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition .
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition .
ModelDataUrl (string) --
The location of the model artifacts. For more information, see ContainerDefinition .
Environment (dict) --
The environment variables to set in the container. For more information, see ContainerDefinition .
(string) --
(string) --
CreationTime (datetime) --
The creation time.
EndTime (datetime) --
The end time.
LastModifiedTime (datetime) --
The last modified time.
FailureReason (string) --
The failure reason.
CandidateProperties (dict) --
The properties of an AutoML candidate job.
CandidateArtifactLocations (dict) --
The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
Explainability (string) --
The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.
ModelInsights (string) --
The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.
BacktestResults (string) --
The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type.
CandidateMetrics (list) --
Information about the candidate metrics for an AutoML job.
(dict) --
Information about the metric for a candidate produced by an AutoML job.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Set (string) --
The dataset split from which the AutoML job produced the metric.
StandardMetricName (string) --
The name of the standard metric.
Note
For definitions of the standard metrics, see ` Autopilot candidate metrics https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-metrics`__ .
InferenceContainerDefinitions (dict) --
The mapping of all supported processing unit (CPU, GPU, etc...) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling CreateAutoMLJobV2 ) related to image or text classification problem types only.
(string) --
Processing unit for an inference container. Currently Autopilot only supports CPU or GPU .
(list) --
Information about the recommended inference container definitions.
(dict) --
A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition .
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition .
ModelDataUrl (string) --
The location of the model artifacts. For more information, see ContainerDefinition .
Environment (dict) --
The environment variables to set in the container. For more information, see ContainerDefinition .
(string) --
(string) --
AutoMLJobStatus (string) --
Returns the status of the AutoML job V2.
AutoMLJobSecondaryStatus (string) --
Returns the secondary status of the AutoML job V2.
AutoMLJobArtifacts (dict) --
The artifacts that are generated during an AutoML job.
CandidateDefinitionNotebookLocation (string) --
The URL of the notebook location.
DataExplorationNotebookLocation (string) --
The URL of the notebook location.
ResolvedAttributes (dict) --
Returns the resolved attributes used by the AutoML job V2.
AutoMLJobObjective (dict) --
Specifies a metric to minimize or maximize as the objective of an AutoML job.
MetricName (string) --
The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.
The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.
For tabular problem types:
List of available metrics:
Regression: InferenceLatency , MAE , MSE , R2 , RMSE
Binary classification: Accuracy , AUC , BalancedAccuracy , F1 , InferenceLatency , LogLoss , Precision , Recall
Multiclass classification: Accuracy , BalancedAccuracy , F1macro , InferenceLatency , LogLoss , PrecisionMacro , RecallMacro
For a description of each metric, see Autopilot metrics for classification and regression .
Default objective metrics:
Regression: MSE .
Binary classification: F1 .
Multiclass classification: Accuracy .
For image or text classification problem types:
List of available metrics: Accuracy For a description of each metric, see Autopilot metrics for text and image classification .
Default objective metrics: Accuracy
For time-series forecasting problem types:
List of available metrics: RMSE , wQL , Average wQL , MASE , MAPE , WAPE For a description of each metric, see Autopilot metrics for time-series forecasting .
Default objective metrics: AverageWeightedQuantileLoss
For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot .
CompletionCriteria (dict) --
How long a job is allowed to run, or how many candidates a job is allowed to generate.
MaxCandidates (integer) --
The maximum number of times a training job is allowed to run.
For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
MaxRuntimePerTrainingJobInSeconds (integer) --
The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.
For job V2s (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.
For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
MaxAutoMLJobRuntimeInSeconds (integer) --
The maximum runtime, in seconds, an AutoML job has to complete.
If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
AutoMLProblemTypeResolvedAttributes (dict) --
Defines the resolved attributes specific to a problem type.
TabularResolvedAttributes (dict) --
The resolved attributes for the tabular problem type.
ProblemType (string) --
The type of supervised learning problem available for the model candidates of the AutoML job V2 (Binary Classification, Multiclass Classification, Regression). For more information, see Amazon SageMaker Autopilot problem types .
TextGenerationResolvedAttributes (dict) --
The resolved attributes for the text generation problem type.
BaseModelName (string) --
The name of the base model to fine-tune.
ModelDeployConfig (dict) --
Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.
AutoGenerateEndpointName (boolean) --
Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False .
Note
If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; otherwise a 400 error is thrown.
EndpointName (string) --
Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.
Note
Specify the EndpointName if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error is thrown.
ModelDeployResult (dict) --
Provides information about endpoint for the model deployment.
EndpointName (string) --
The name of the endpoint to which the model has been deployed.
Note
If model deployment fails, this field is omitted from the response.
DataSplitConfig (dict) --
Returns the configuration settings of how the data are split into train and validation datasets.
ValidationFraction (float) --
The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.
SecurityConfig (dict) --
Returns the security configuration for traffic encryption or Amazon VPC settings.
VolumeKmsKeyId (string) --
The key used to encrypt stored data.
EnableInterContainerTrafficEncryption (boolean) --
Whether to use traffic encryption between the container layers.
VpcConfig (dict) --
The VPC configuration.
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
{'OutputConfig': {'TargetDevice': {'ml_c6g', 'ml_m6g', 'rasp4b'}}}
Returns information about a model compilation job.
To create a model compilation job, use CreateCompilationJob . To get information about multiple model compilation jobs, use ListCompilationJobs .
See also: AWS API Documentation
Request Syntax
client.describe_compilation_job(
CompilationJobName='string'
)
string
[REQUIRED]
The name of the model compilation job that you want information about.
dict
Response Syntax
{
'CompilationJobName': 'string',
'CompilationJobArn': 'string',
'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
'CompilationStartTime': datetime(2015, 1, 1),
'CompilationEndTime': datetime(2015, 1, 1),
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
},
'InferenceImage': 'string',
'ModelPackageVersionArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'ModelDigests': {
'ArtifactDigest': 'string'
},
'RoleArn': 'string',
'InputConfig': {
'S3Uri': 'string',
'DataInputConfig': 'string',
'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'|'TFLITE'|'DARKNET'|'SKLEARN',
'FrameworkVersion': 'string'
},
'OutputConfig': {
'S3OutputLocation': 'string',
'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_m6g'|'ml_c4'|'ml_c5'|'ml_c6g'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'ml_inf2'|'ml_trn1'|'ml_eia2'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'rasp4b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv2'|'amba_cv22'|'amba_cv25'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm'|'imx8mplus',
'TargetPlatform': {
'Os': 'ANDROID'|'LINUX',
'Arch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
'Accelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'|'NNA'
},
'CompilerOptions': 'string',
'KmsKeyId': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'DerivedInformation': {
'DerivedDataInputConfig': 'string'
}
}
Response Structure
(dict) --
CompilationJobName (string) --
The name of the model compilation job.
CompilationJobArn (string) --
The Amazon Resource Name (ARN) of the model compilation job.
CompilationJobStatus (string) --
The status of the model compilation job.
CompilationStartTime (datetime) --
The time when the model compilation job started the CompilationJob instances.
You are billed for the time between this timestamp and the timestamp in the CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That's because it takes time to download the compilation job, which depends on the size of the compilation job container.
CompilationEndTime (datetime) --
The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job's model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker detected that the job failed.
StoppingCondition (dict) --
Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
InferenceImage (string) --
The inference image to use when compiling a model. Specify an image only if the target device is a cloud instance.
ModelPackageVersionArn (string) --
The Amazon Resource Name (ARN) of the versioned model package that was provided to SageMaker Neo when you initiated a compilation job.
CreationTime (datetime) --
The time that the model compilation job was created.
LastModifiedTime (datetime) --
The time that the status of the model compilation job was last modified.
FailureReason (string) --
If a model compilation job failed, the reason it failed.
ModelArtifacts (dict) --
Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
ModelDigests (dict) --
Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3.
ArtifactDigest (string) --
Provides a hash value that uniquely identifies the stored model artifacts.
RoleArn (string) --
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.
InputConfig (dict) --
Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
S3Uri (string) --
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
DataInputConfig (string) --
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are Framework specific.
TensorFlow : You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input":[1,1024,1024,3]}
If using the CLI, {\"input\":[1,1024,1024,3]}
Examples for two inputs:
If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
KERAS : You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"input_1":[1,3,224,224]}
If using the CLI, {\"input_1\":[1,3,224,224]}
Examples for two inputs:
If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}
If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}
MXNET/ONNX/DARKNET : You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.
Examples for one input:
If using the console, {"data":[1,3,1024,1024]}
If using the CLI, {\"data\":[1,3,1024,1024]}
Examples for two inputs:
If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
PyTorch : You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.
Examples for one input in dictionary format:
If using the console, {"input0":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224]}
Example for one input in list format: [[1,3,224,224]]
Examples for two inputs in dictionary format:
If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]
XGBOOST : input data name and shape are not needed.
DataInputConfig supports the following parameters for CoreML TargetDevice (ML Model format):
shape : Input shape, for example {"input_1": {"shape": [1,224,224,3]}} . In addition to static input shapes, CoreML converter supports Flexible input shapes:
Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}
Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example: {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
default_shape : Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}
type : Input type. Allowed values: Image and Tensor . By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such as bias and scale .
bias : If the input type is an Image, you need to provide the bias vector.
scale : If the input type is an Image, you need to provide a scale factor.
CoreML ClassifierConfig parameters can be specified using OutputConfig CompilerOptions . CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:
Tensor type input:
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}
Tensor type input without input name (PyTorch):
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]
Image type input:
"DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
Image type input without input name (PyTorch):
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
"CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}
Depending on the model format, DataInputConfig requires the following parameters for ml_eia2 OutputConfig:TargetDevice .
For TensorFlow models saved in the SavedModel format, specify the input names from signature_def_key and the input model shapes for DataInputConfig . Specify the signature_def_key in ` OutputConfig:CompilerOptions https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html#sagemaker-Type-OutputConfig-CompilerOptions`__ if the model does not use TensorFlow's default signature def key. For example:
"DataInputConfig": {"inputs": [1, 224, 224, 3]}
"CompilerOptions": {"signature_def_key": "serving_custom"}
For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in DataInputConfig and the output tensor names for output_names in ` OutputConfig:CompilerOptions https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html#sagemaker-Type-OutputConfig-CompilerOptions`__ . For example:
"DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}
"CompilerOptions": {"output_names": ["output_tensor:0"]}
Framework (string) --
Identifies the framework in which the model was trained. For example: TENSORFLOW.
FrameworkVersion (string) --
Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.
For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks .
OutputConfig (dict) --
Information about the output location for the compiled model and the target device that the model runs on.
S3OutputLocation (string) --
Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
TargetDevice (string) --
Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform .
Note
Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.
TargetPlatform (dict) --
Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice .
The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
Raspberry Pi 3 Model B+ "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"}, "CompilerOptions": {'mattr': ['+neon']}
Jetson TX2 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
EC2 m5.2xlarge instance OS "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"}, "CompilerOptions": {'mcpu': 'skylake-avx512'}
RK3399 "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
ARMv7 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"}, "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
ARMv8 phone (CPU) "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"}, "CompilerOptions": {'ANDROID_PLATFORM': 29}
Os (string) --
Specifies a target platform OS.
LINUX : Linux-based operating systems.
ANDROID : Android operating systems. Android API level can be specified using the ANDROID_PLATFORM compiler option. For example, "CompilerOptions": {'ANDROID_PLATFORM': 28}
Arch (string) --
Specifies a target platform architecture.
X86_64 : 64-bit version of the x86 instruction set.
X86 : 32-bit version of the x86 instruction set.
ARM64 : ARMv8 64-bit CPU.
ARM_EABIHF : ARMv7 32-bit, Hard Float.
ARM_EABI : ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.
Accelerator (string) --
Specifies a target platform accelerator (optional).
NVIDIA : Nvidia graphics processing unit. It also requires gpu-code , trt-ver , cuda-ver compiler options
MALI : ARM Mali graphics processor
INTEL_GRAPHICS : Integrated Intel graphics
CompilerOptions (string) --
Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.
DTYPE : Specifies the data type for the input. When compiling for ml_* (except for ml_inf ) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:
float32: Use either "float" or "float32" .
int64: Use either "int64" or "long" .
For example, {"dtype" : "float32"} .
CPU : Compilation for CPU supports the following compiler options.
mcpu : CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
mattr : CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
ARM : Details of ARM CPU compilations.
NEON : NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors. For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
NVIDIA : Compilation for NVIDIA GPU supports the following compiler options.
gpu_code : Specifies the targeted architecture.
trt-ver : Specifies the TensorRT versions in x.y.z. format.
cuda-ver : Specifies the CUDA version in x.y format.
For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
ANDROID : Compilation for the Android OS supports the following compiler options:
ANDROID_PLATFORM : Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28} .
mattr : Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.
INFERENTIA : Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"" . For information about supported compiler options, see Neuron Compiler CLI Reference Guide .
CoreML : Compilation for the CoreML OutputConfig TargetDevice supports the following compiler options:
class_labels : Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"} . Labels inside the txt file should be separated by newlines.
EIA : Compilation for the Elastic Inference Accelerator supports the following compiler options:
precision_mode : Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32" . Default is "FP32" .
signature_def_key : Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.
output_names : Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names .
For example: {"precision_mode": "FP32", "output_names": ["output:0"]}
KmsKeyId (string) --
The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs. IDs have the form of sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.
(string) --
DerivedInformation (dict) --
Information that SageMaker Neo automatically derived about the model.
DerivedDataInputConfig (string) --
The data input configuration that SageMaker Neo automatically derived for the model. When SageMaker Neo derives this information, you don't need to specify the data input configuration when you create a compilation job.
{'PendingDeploymentSummary': {'ProductionVariants': {'InstanceType': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}},
'ShadowProductionVariants': {'InstanceType': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}}}
Returns the description of an endpoint.
See also: AWS API Documentation
Request Syntax
client.describe_endpoint(
EndpointName='string'
)
string
[REQUIRED]
The name of the endpoint.
dict
Response Syntax
{
'EndpointName': 'string',
'EndpointArn': 'string',
'EndpointConfigName': 'string',
'ProductionVariants': [
{
'VariantName': 'string',
'DeployedImages': [
{
'SpecifiedImage': 'string',
'ResolvedImage': 'string',
'ResolutionTime': datetime(2015, 1, 1)
},
],
'CurrentWeight': ...,
'DesiredWeight': ...,
'CurrentInstanceCount': 123,
'DesiredInstanceCount': 123,
'VariantStatus': [
{
'Status': 'Creating'|'Updating'|'Deleting'|'ActivatingTraffic'|'Baking',
'StatusMessage': 'string',
'StartTime': datetime(2015, 1, 1)
},
],
'CurrentServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'DesiredServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'ManagedInstanceScaling': {
'Status': 'ENABLED'|'DISABLED',
'MinInstanceCount': 123,
'MaxInstanceCount': 123
},
'RoutingConfig': {
'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
}
},
],
'DataCaptureConfig': {
'EnableCapture': True|False,
'CaptureStatus': 'Started'|'Stopped',
'CurrentSamplingPercentage': 123,
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
},
'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed'|'UpdateRollbackFailed',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastDeploymentConfig': {
'BlueGreenUpdatePolicy': {
'TrafficRoutingConfiguration': {
'Type': 'ALL_AT_ONCE'|'CANARY'|'LINEAR',
'WaitIntervalInSeconds': 123,
'CanarySize': {
'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
'Value': 123
},
'LinearStepSize': {
'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
'Value': 123
}
},
'TerminationWaitInSeconds': 123,
'MaximumExecutionTimeoutInSeconds': 123
},
'RollingUpdatePolicy': {
'MaximumBatchSize': {
'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
'Value': 123
},
'WaitIntervalInSeconds': 123,
'MaximumExecutionTimeoutInSeconds': 123,
'RollbackMaximumBatchSize': {
'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENT',
'Value': 123
}
},
'AutoRollbackConfiguration': {
'Alarms': [
{
'AlarmName': 'string'
},
]
}
},
'AsyncInferenceConfig': {
'ClientConfig': {
'MaxConcurrentInvocationsPerInstance': 123
},
'OutputConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'NotificationConfig': {
'SuccessTopic': 'string',
'ErrorTopic': 'string',
'IncludeInferenceResponseIn': [
'SUCCESS_NOTIFICATION_TOPIC'|'ERROR_NOTIFICATION_TOPIC',
]
},
'S3FailurePath': 'string'
}
},
'PendingDeploymentSummary': {
'EndpointConfigName': 'string',
'ProductionVariants': [
{
'VariantName': 'string',
'DeployedImages': [
{
'SpecifiedImage': 'string',
'ResolvedImage': 'string',
'ResolutionTime': datetime(2015, 1, 1)
},
],
'CurrentWeight': ...,
'DesiredWeight': ...,
'CurrentInstanceCount': 123,
'DesiredInstanceCount': 123,
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
'VariantStatus': [
{
'Status': 'Creating'|'Updating'|'Deleting'|'ActivatingTraffic'|'Baking',
'StatusMessage': 'string',
'StartTime': datetime(2015, 1, 1)
},
],
'CurrentServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'DesiredServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'ManagedInstanceScaling': {
'Status': 'ENABLED'|'DISABLED',
'MinInstanceCount': 123,
'MaxInstanceCount': 123
},
'RoutingConfig': {
'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
}
},
],
'StartTime': datetime(2015, 1, 1),
'ShadowProductionVariants': [
{
'VariantName': 'string',
'DeployedImages': [
{
'SpecifiedImage': 'string',
'ResolvedImage': 'string',
'ResolutionTime': datetime(2015, 1, 1)
},
],
'CurrentWeight': ...,
'DesiredWeight': ...,
'CurrentInstanceCount': 123,
'DesiredInstanceCount': 123,
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
'VariantStatus': [
{
'Status': 'Creating'|'Updating'|'Deleting'|'ActivatingTraffic'|'Baking',
'StatusMessage': 'string',
'StartTime': datetime(2015, 1, 1)
},
],
'CurrentServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'DesiredServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'ManagedInstanceScaling': {
'Status': 'ENABLED'|'DISABLED',
'MinInstanceCount': 123,
'MaxInstanceCount': 123
},
'RoutingConfig': {
'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
}
},
]
},
'ExplainerConfig': {
'ClarifyExplainerConfig': {
'EnableExplanations': 'string',
'InferenceConfig': {
'FeaturesAttribute': 'string',
'ContentTemplate': 'string',
'MaxRecordCount': 123,
'MaxPayloadInMB': 123,
'ProbabilityIndex': 123,
'LabelIndex': 123,
'ProbabilityAttribute': 'string',
'LabelAttribute': 'string',
'LabelHeaders': [
'string',
],
'FeatureHeaders': [
'string',
],
'FeatureTypes': [
'numerical'|'categorical'|'text',
]
},
'ShapConfig': {
'ShapBaselineConfig': {
'MimeType': 'string',
'ShapBaseline': 'string',
'ShapBaselineUri': 'string'
},
'NumberOfSamples': 123,
'UseLogit': True|False,
'Seed': 123,
'TextConfig': {
'Language': 'af'|'sq'|'ar'|'hy'|'eu'|'bn'|'bg'|'ca'|'zh'|'hr'|'cs'|'da'|'nl'|'en'|'et'|'fi'|'fr'|'de'|'el'|'gu'|'he'|'hi'|'hu'|'is'|'id'|'ga'|'it'|'kn'|'ky'|'lv'|'lt'|'lb'|'mk'|'ml'|'mr'|'ne'|'nb'|'fa'|'pl'|'pt'|'ro'|'ru'|'sa'|'sr'|'tn'|'si'|'sk'|'sl'|'es'|'sv'|'tl'|'ta'|'tt'|'te'|'tr'|'uk'|'ur'|'yo'|'lij'|'xx',
'Granularity': 'token'|'sentence'|'paragraph'
}
}
}
},
'ShadowProductionVariants': [
{
'VariantName': 'string',
'DeployedImages': [
{
'SpecifiedImage': 'string',
'ResolvedImage': 'string',
'ResolutionTime': datetime(2015, 1, 1)
},
],
'CurrentWeight': ...,
'DesiredWeight': ...,
'CurrentInstanceCount': 123,
'DesiredInstanceCount': 123,
'VariantStatus': [
{
'Status': 'Creating'|'Updating'|'Deleting'|'ActivatingTraffic'|'Baking',
'StatusMessage': 'string',
'StartTime': datetime(2015, 1, 1)
},
],
'CurrentServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'DesiredServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'ManagedInstanceScaling': {
'Status': 'ENABLED'|'DISABLED',
'MinInstanceCount': 123,
'MaxInstanceCount': 123
},
'RoutingConfig': {
'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
}
},
]
}
Response Structure
(dict) --
EndpointName (string) --
Name of the endpoint.
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
EndpointConfigName (string) --
The name of the endpoint configuration associated with this endpoint.
ProductionVariants (list) --
An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.
(dict) --
Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating , you get different desired and current values.
VariantName (string) --
The name of the variant.
DeployedImages (list) --
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .
(dict) --
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .
If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .
SpecifiedImage (string) --
The image path you specified when you created the model.
ResolvedImage (string) --
The specific digest path of the image hosted in this ProductionVariant .
ResolutionTime (datetime) --
The date and time when the image path for the model resolved to the ResolvedImage
CurrentWeight (float) --
The weight associated with the variant.
DesiredWeight (float) --
The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
CurrentInstanceCount (integer) --
The number of instances associated with the variant.
DesiredInstanceCount (integer) --
The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
VariantStatus (list) --
The endpoint variant status which describes the current deployment stage status or operational status.
(dict) --
Describes the status of the production variant.
Status (string) --
The endpoint variant status which describes the current deployment stage status or operational status.
Creating : Creating inference resources for the production variant.
Deleting : Terminating inference resources for the production variant.
Updating : Updating capacity for the production variant.
ActivatingTraffic : Turning on traffic for the production variant.
Baking : Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
StatusMessage (string) --
A message that describes the status of the production variant.
StartTime (datetime) --
The start time of the current status change.
CurrentServerlessConfig (dict) --
The serverless configuration for the endpoint.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
DesiredServerlessConfig (dict) --
The serverless configuration requested for the endpoint update.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
ManagedInstanceScaling (dict) --
Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status (string) --
Indicates whether managed instance scaling is enabled.
MinInstanceCount (integer) --
The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount (integer) --
The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig (dict) --
Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy (string) --
Sets how the endpoint routes incoming traffic:
LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.
RANDOM : The endpoint routes each request to a randomly chosen instance.
DataCaptureConfig (dict) --
The currently active data capture configuration used by your Endpoint.
EnableCapture (boolean) --
Whether data capture is enabled or disabled.
CaptureStatus (string) --
Whether data capture is currently functional.
CurrentSamplingPercentage (integer) --
The percentage of requests being captured by your Endpoint.
DestinationS3Uri (string) --
The Amazon S3 location being used to capture the data.
KmsKeyId (string) --
The KMS key being used to encrypt the data in Amazon S3.
EndpointStatus (string) --
The status of the endpoint.
OutOfService : Endpoint is not available to take incoming requests.
Creating : CreateEndpoint is executing.
Updating : UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.
SystemUpdating : Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count.
RollingBack : Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.
InService : Endpoint is available to process incoming requests.
Deleting : DeleteEndpoint is executing.
Failed : Endpoint could not be created, updated, or re-scaled. Use the FailureReason value returned by DescribeEndpoint for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.
UpdateRollbackFailed : Both the rolling deployment and auto-rollback failed. Your endpoint is in service with a mix of the old and new endpoint configurations. For information about how to remedy this issue and restore the endpoint's status to InService , see Rolling Deployments .
FailureReason (string) --
If the status of the endpoint is Failed , the reason why it failed.
CreationTime (datetime) --
A timestamp that shows when the endpoint was created.
LastModifiedTime (datetime) --
A timestamp that shows when the endpoint was last modified.
LastDeploymentConfig (dict) --
The most recent deployment configuration for the endpoint.
BlueGreenUpdatePolicy (dict) --
Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.
TrafficRoutingConfiguration (dict) --
Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.
Type (string) --
Traffic routing strategy type.
ALL_AT_ONCE : Endpoint traffic shifts to the new fleet in a single step.
CANARY : Endpoint traffic shifts to the new fleet in two steps. The first step is the canary, which is a small portion of the traffic. The second step is the remainder of the traffic.
LINEAR : Endpoint traffic shifts to the new fleet in n steps of a configurable size.
WaitIntervalInSeconds (integer) --
The waiting time (in seconds) between incremental steps to turn on traffic on the new endpoint fleet.
CanarySize (dict) --
Batch size for the first step to turn on traffic on the new endpoint fleet. Value must be less than or equal to 50% of the variant's total instance count.
Type (string) --
Specifies the endpoint capacity type.
INSTANCE_COUNT : The endpoint activates based on the number of instances.
CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.
Value (integer) --
Defines the capacity size, either as a number of instances or a capacity percentage.
LinearStepSize (dict) --
Batch size for each step to turn on traffic on the new endpoint fleet. Value must be 10-50% of the variant's total instance count.
Type (string) --
Specifies the endpoint capacity type.
INSTANCE_COUNT : The endpoint activates based on the number of instances.
CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.
Value (integer) --
Defines the capacity size, either as a number of instances or a capacity percentage.
TerminationWaitInSeconds (integer) --
Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.
MaximumExecutionTimeoutInSeconds (integer) --
Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in TerminationWaitInSeconds and WaitIntervalInSeconds .
RollingUpdatePolicy (dict) --
Specifies a rolling deployment strategy for updating a SageMaker endpoint.
MaximumBatchSize (dict) --
Batch size for each rolling step to provision capacity and turn on traffic on the new endpoint fleet, and terminate capacity on the old endpoint fleet. Value must be between 5% to 50% of the variant's total instance count.
Type (string) --
Specifies the endpoint capacity type.
INSTANCE_COUNT : The endpoint activates based on the number of instances.
CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.
Value (integer) --
Defines the capacity size, either as a number of instances or a capacity percentage.
WaitIntervalInSeconds (integer) --
The length of the baking period, during which SageMaker monitors alarms for each batch on the new fleet.
MaximumExecutionTimeoutInSeconds (integer) --
The time limit for the total deployment. Exceeding this limit causes a timeout.
RollbackMaximumBatchSize (dict) --
Batch size for rollback to the old endpoint fleet. Each rolling step to provision capacity and turn on traffic on the old endpoint fleet, and terminate capacity on the new endpoint fleet. If this field is absent, the default value will be set to 100% of total capacity which means to bring up the whole capacity of the old fleet at once during rollback.
Type (string) --
Specifies the endpoint capacity type.
INSTANCE_COUNT : The endpoint activates based on the number of instances.
CAPACITY_PERCENT : The endpoint activates based on the specified percentage of capacity.
Value (integer) --
Defines the capacity size, either as a number of instances or a capacity percentage.
AutoRollbackConfiguration (dict) --
Automatic rollback configuration for handling endpoint deployment failures and recovery.
Alarms (list) --
List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.
(dict) --
An Amazon CloudWatch alarm configured to monitor metrics on an endpoint.
AlarmName (string) --
The name of a CloudWatch alarm in your account.
AsyncInferenceConfig (dict) --
Returns the description of an endpoint configuration created using the ` CreateEndpointConfig https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html`__ API.
ClientConfig (dict) --
Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.
MaxConcurrentInvocationsPerInstance (integer) --
The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.
OutputConfig (dict) --
Specifies the configuration for asynchronous inference invocation outputs.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
S3OutputPath (string) --
The Amazon S3 location to upload inference responses to.
NotificationConfig (dict) --
Specifies the configuration for notifications of inference results for asynchronous inference.
SuccessTopic (string) --
Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.
ErrorTopic (string) --
Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.
IncludeInferenceResponseIn (list) --
The Amazon SNS topics where you want the inference response to be included.
Note
The inference response is included only if the response size is less than or equal to 128 KB.
(string) --
S3FailurePath (string) --
The Amazon S3 location to upload failure inference responses to.
PendingDeploymentSummary (dict) --
Returns the summary of an in-progress deployment. This field is only returned when the endpoint is creating or updating with a new endpoint configuration.
EndpointConfigName (string) --
The name of the endpoint configuration used in the deployment.
ProductionVariants (list) --
An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint for the in-progress deployment.
(dict) --
The production variant summary for a deployment when an endpoint is creating or updating with the CreateEndpoint or UpdateEndpoint operations. Describes the VariantStatus , weight and capacity for a production variant associated with an endpoint.
VariantName (string) --
The name of the variant.
DeployedImages (list) --
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .
(dict) --
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .
If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .
SpecifiedImage (string) --
The image path you specified when you created the model.
ResolvedImage (string) --
The specific digest path of the image hosted in this ProductionVariant .
ResolutionTime (datetime) --
The date and time when the image path for the model resolved to the ResolvedImage
CurrentWeight (float) --
The weight associated with the variant.
DesiredWeight (float) --
The requested weight for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
CurrentInstanceCount (integer) --
The number of instances associated with the variant.
DesiredInstanceCount (integer) --
The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
InstanceType (string) --
The type of instances associated with the variant.
AcceleratorType (string) --
The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .
VariantStatus (list) --
The endpoint variant status which describes the current deployment stage status or operational status.
(dict) --
Describes the status of the production variant.
Status (string) --
The endpoint variant status which describes the current deployment stage status or operational status.
Creating : Creating inference resources for the production variant.
Deleting : Terminating inference resources for the production variant.
Updating : Updating capacity for the production variant.
ActivatingTraffic : Turning on traffic for the production variant.
Baking : Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
StatusMessage (string) --
A message that describes the status of the production variant.
StartTime (datetime) --
The start time of the current status change.
CurrentServerlessConfig (dict) --
The serverless configuration for the endpoint.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
DesiredServerlessConfig (dict) --
The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
ManagedInstanceScaling (dict) --
Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status (string) --
Indicates whether managed instance scaling is enabled.
MinInstanceCount (integer) --
The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount (integer) --
The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig (dict) --
Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy (string) --
Sets how the endpoint routes incoming traffic:
LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.
RANDOM : The endpoint routes each request to a randomly chosen instance.
StartTime (datetime) --
The start time of the deployment.
ShadowProductionVariants (list) --
An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants for the in-progress deployment.
(dict) --
The production variant summary for a deployment when an endpoint is creating or updating with the CreateEndpoint or UpdateEndpoint operations. Describes the VariantStatus , weight and capacity for a production variant associated with an endpoint.
VariantName (string) --
The name of the variant.
DeployedImages (list) --
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .
(dict) --
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .
If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .
SpecifiedImage (string) --
The image path you specified when you created the model.
ResolvedImage (string) --
The specific digest path of the image hosted in this ProductionVariant .
ResolutionTime (datetime) --
The date and time when the image path for the model resolved to the ResolvedImage
CurrentWeight (float) --
The weight associated with the variant.
DesiredWeight (float) --
The requested weight for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
CurrentInstanceCount (integer) --
The number of instances associated with the variant.
DesiredInstanceCount (integer) --
The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
InstanceType (string) --
The type of instances associated with the variant.
AcceleratorType (string) --
The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .
VariantStatus (list) --
The endpoint variant status which describes the current deployment stage status or operational status.
(dict) --
Describes the status of the production variant.
Status (string) --
The endpoint variant status which describes the current deployment stage status or operational status.
Creating : Creating inference resources for the production variant.
Deleting : Terminating inference resources for the production variant.
Updating : Updating capacity for the production variant.
ActivatingTraffic : Turning on traffic for the production variant.
Baking : Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
StatusMessage (string) --
A message that describes the status of the production variant.
StartTime (datetime) --
The start time of the current status change.
CurrentServerlessConfig (dict) --
The serverless configuration for the endpoint.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
DesiredServerlessConfig (dict) --
The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
ManagedInstanceScaling (dict) --
Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status (string) --
Indicates whether managed instance scaling is enabled.
MinInstanceCount (integer) --
The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount (integer) --
The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig (dict) --
Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy (string) --
Sets how the endpoint routes incoming traffic:
LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.
RANDOM : The endpoint routes each request to a randomly chosen instance.
ExplainerConfig (dict) --
The configuration parameters for an explainer.
ClarifyExplainerConfig (dict) --
A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.
EnableExplanations (string) --
A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See ` EnableExplanations https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-enable`__ for additional information.
InferenceConfig (dict) --
The inference configuration parameter for the model container.
FeaturesAttribute (string) --
Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures' , it extracts a list of features [1,2,3] from request data '{"myfeatures":[1,2,3]}' .
ContentTemplate (string) --
A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features [1,2,3] into the record string '{"myfeatures":[1,2,3]}' . Required only when the model container input is in JSON Lines format.
MaxRecordCount (integer) --
The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset . A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1 , the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.
MaxPayloadInMB (integer) --
The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.
ProbabilityIndex (integer) --
A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.
Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6' , set ProbabilityIndex to 1 to select the probability value 0.6 .
Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3] .
LabelIndex (integer) --
A zero-based index used to extract a label header or list of label headers from model container output in CSV format.
Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set LabelIndex to 0 to select the label headers ['cat','dog','fish'] .
ProbabilityAttribute (string) --
A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.
Example : If the model container output of a single request is '{"predicted_label":1,"probability":0.6}' , then set ProbabilityAttribute to 'probability' .
LabelAttribute (string) --
A JMESPath expression used to locate the list of label headers in the model container output.
Example : If the model container output of a batch request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}' , then set LabelAttribute to 'labels' to extract the list of label headers ["cat","dog","fish"]
LabelHeaders (list) --
For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.
(string) --
FeatureHeaders (list) --
The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.
(string) --
FeatureTypes (list) --
A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, ['text'] ). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.
(string) --
ShapConfig (dict) --
The configuration for SHAP analysis.
ShapBaselineConfig (dict) --
The configuration for the SHAP baseline of the Kernal SHAP algorithm.
MimeType (string) --
The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines' . Defaults to 'text/csv' .
ShapBaseline (string) --
The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.
ShapBaselineUri (string) --
The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud .
NumberOfSamples (integer) --
The number of samples to be used for analysis by the Kernal SHAP algorithm.
Note
The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint .
UseLogit (boolean) --
A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.
Seed (integer) --
The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.
TextConfig (dict) --
A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.
Language (string) --
Specifies the language of the text features in `ISO 639-1 < https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes>`__ or ISO 639-3 code of a supported language.
Note
For a mix of multiple languages, use code 'xx' .
Granularity (string) --
The unit of granularity for the analysis of text features. For example, if the unit is 'token' , then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.
ShadowProductionVariants (list) --
An array of ProductionVariantSummary objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants .
(dict) --
Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating , you get different desired and current values.
VariantName (string) --
The name of the variant.
DeployedImages (list) --
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .
(dict) --
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .
If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .
SpecifiedImage (string) --
The image path you specified when you created the model.
ResolvedImage (string) --
The specific digest path of the image hosted in this ProductionVariant .
ResolutionTime (datetime) --
The date and time when the image path for the model resolved to the ResolvedImage
CurrentWeight (float) --
The weight associated with the variant.
DesiredWeight (float) --
The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
CurrentInstanceCount (integer) --
The number of instances associated with the variant.
DesiredInstanceCount (integer) --
The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
VariantStatus (list) --
The endpoint variant status which describes the current deployment stage status or operational status.
(dict) --
Describes the status of the production variant.
Status (string) --
The endpoint variant status which describes the current deployment stage status or operational status.
Creating : Creating inference resources for the production variant.
Deleting : Terminating inference resources for the production variant.
Updating : Updating capacity for the production variant.
ActivatingTraffic : Turning on traffic for the production variant.
Baking : Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
StatusMessage (string) --
A message that describes the status of the production variant.
StartTime (datetime) --
The start time of the current status change.
CurrentServerlessConfig (dict) --
The serverless configuration for the endpoint.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
DesiredServerlessConfig (dict) --
The serverless configuration requested for the endpoint update.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
ManagedInstanceScaling (dict) --
Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status (string) --
Indicates whether managed instance scaling is enabled.
MinInstanceCount (integer) --
The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount (integer) --
The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig (dict) --
Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy (string) --
Sets how the endpoint routes incoming traffic:
LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.
RANDOM : The endpoint routes each request to a randomly chosen instance.
{'DataCaptureConfig': {'CaptureOptions': {'CaptureMode': {'InputAndOutput'}}},
'ProductionVariants': {'InstanceType': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}},
'ShadowProductionVariants': {'InstanceType': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}}
Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
See also: AWS API Documentation
Request Syntax
client.describe_endpoint_config(
EndpointConfigName='string'
)
string
[REQUIRED]
The name of the endpoint configuration.
dict
Response Syntax
{
'EndpointConfigName': 'string',
'EndpointConfigArn': 'string',
'ProductionVariants': [
{
'VariantName': 'string',
'ModelName': 'string',
'InitialInstanceCount': 123,
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
'InitialVariantWeight': ...,
'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
'CoreDumpConfig': {
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
},
'ServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'VolumeSizeInGB': 123,
'ModelDataDownloadTimeoutInSeconds': 123,
'ContainerStartupHealthCheckTimeoutInSeconds': 123,
'EnableSSMAccess': True|False,
'ManagedInstanceScaling': {
'Status': 'ENABLED'|'DISABLED',
'MinInstanceCount': 123,
'MaxInstanceCount': 123
},
'RoutingConfig': {
'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
}
},
],
'DataCaptureConfig': {
'EnableCapture': True|False,
'InitialSamplingPercentage': 123,
'DestinationS3Uri': 'string',
'KmsKeyId': 'string',
'CaptureOptions': [
{
'CaptureMode': 'Input'|'Output'|'InputAndOutput'
},
],
'CaptureContentTypeHeader': {
'CsvContentTypes': [
'string',
],
'JsonContentTypes': [
'string',
]
}
},
'KmsKeyId': 'string',
'CreationTime': datetime(2015, 1, 1),
'AsyncInferenceConfig': {
'ClientConfig': {
'MaxConcurrentInvocationsPerInstance': 123
},
'OutputConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'NotificationConfig': {
'SuccessTopic': 'string',
'ErrorTopic': 'string',
'IncludeInferenceResponseIn': [
'SUCCESS_NOTIFICATION_TOPIC'|'ERROR_NOTIFICATION_TOPIC',
]
},
'S3FailurePath': 'string'
}
},
'ExplainerConfig': {
'ClarifyExplainerConfig': {
'EnableExplanations': 'string',
'InferenceConfig': {
'FeaturesAttribute': 'string',
'ContentTemplate': 'string',
'MaxRecordCount': 123,
'MaxPayloadInMB': 123,
'ProbabilityIndex': 123,
'LabelIndex': 123,
'ProbabilityAttribute': 'string',
'LabelAttribute': 'string',
'LabelHeaders': [
'string',
],
'FeatureHeaders': [
'string',
],
'FeatureTypes': [
'numerical'|'categorical'|'text',
]
},
'ShapConfig': {
'ShapBaselineConfig': {
'MimeType': 'string',
'ShapBaseline': 'string',
'ShapBaselineUri': 'string'
},
'NumberOfSamples': 123,
'UseLogit': True|False,
'Seed': 123,
'TextConfig': {
'Language': 'af'|'sq'|'ar'|'hy'|'eu'|'bn'|'bg'|'ca'|'zh'|'hr'|'cs'|'da'|'nl'|'en'|'et'|'fi'|'fr'|'de'|'el'|'gu'|'he'|'hi'|'hu'|'is'|'id'|'ga'|'it'|'kn'|'ky'|'lv'|'lt'|'lb'|'mk'|'ml'|'mr'|'ne'|'nb'|'fa'|'pl'|'pt'|'ro'|'ru'|'sa'|'sr'|'tn'|'si'|'sk'|'sl'|'es'|'sv'|'tl'|'ta'|'tt'|'te'|'tr'|'uk'|'ur'|'yo'|'lij'|'xx',
'Granularity': 'token'|'sentence'|'paragraph'
}
}
}
},
'ShadowProductionVariants': [
{
'VariantName': 'string',
'ModelName': 'string',
'InitialInstanceCount': 123,
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
'InitialVariantWeight': ...,
'AcceleratorType': 'ml.eia1.medium'|'ml.eia1.large'|'ml.eia1.xlarge'|'ml.eia2.medium'|'ml.eia2.large'|'ml.eia2.xlarge',
'CoreDumpConfig': {
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
},
'ServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'VolumeSizeInGB': 123,
'ModelDataDownloadTimeoutInSeconds': 123,
'ContainerStartupHealthCheckTimeoutInSeconds': 123,
'EnableSSMAccess': True|False,
'ManagedInstanceScaling': {
'Status': 'ENABLED'|'DISABLED',
'MinInstanceCount': 123,
'MaxInstanceCount': 123
},
'RoutingConfig': {
'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
}
},
],
'ExecutionRoleArn': 'string',
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'EnableNetworkIsolation': True|False
}
Response Structure
(dict) --
EndpointConfigName (string) --
Name of the SageMaker endpoint configuration.
EndpointConfigArn (string) --
The Amazon Resource Name (ARN) of the endpoint configuration.
ProductionVariants (list) --
An array of ProductionVariant objects, one for each model that you want to host at this endpoint.
(dict) --
Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants .
VariantName (string) --
The name of the production variant.
ModelName (string) --
The name of the model that you want to host. This is the name that you specified when creating the model.
InitialInstanceCount (integer) --
Number of instances to launch initially.
InstanceType (string) --
The ML compute instance type.
InitialVariantWeight (float) --
Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.
AcceleratorType (string) --
The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .
CoreDumpConfig (dict) --
Specifies configuration for a core dump from the model container when the process crashes.
DestinationS3Uri (string) --
The Amazon S3 bucket to send the core dump to.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
ServerlessConfig (dict) --
The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.
ModelDataDownloadTimeoutInSeconds (integer) --
The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.
ContainerStartupHealthCheckTimeoutInSeconds (integer) --
The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .
EnableSSMAccess (boolean) --
You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint .
ManagedInstanceScaling (dict) --
Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status (string) --
Indicates whether managed instance scaling is enabled.
MinInstanceCount (integer) --
The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount (integer) --
The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig (dict) --
Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy (string) --
Sets how the endpoint routes incoming traffic:
LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.
RANDOM : The endpoint routes each request to a randomly chosen instance.
DataCaptureConfig (dict) --
Configuration to control how SageMaker captures inference data.
EnableCapture (boolean) --
Whether data capture should be enabled or disabled (defaults to enabled).
InitialSamplingPercentage (integer) --
The percentage of requests SageMaker will capture. A lower value is recommended for Endpoints with high traffic.
DestinationS3Uri (string) --
The Amazon S3 location used to capture the data.
KmsKeyId (string) --
The Amazon Resource Name (ARN) of an Key Management Service key that SageMaker uses to encrypt the captured data at rest using Amazon S3 server-side encryption.
The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
CaptureOptions (list) --
Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both
(dict) --
Specifies data Model Monitor will capture.
CaptureMode (string) --
Specify the boundary of data to capture.
CaptureContentTypeHeader (dict) --
Configuration specifying how to treat different headers. If no headers are specified SageMaker will by default base64 encode when capturing the data.
CsvContentTypes (list) --
The list of all content type headers that Amazon SageMaker will treat as CSV and capture accordingly.
(string) --
JsonContentTypes (list) --
The list of all content type headers that SageMaker will treat as JSON and capture accordingly.
(string) --
KmsKeyId (string) --
Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
CreationTime (datetime) --
A timestamp that shows when the endpoint configuration was created.
AsyncInferenceConfig (dict) --
Returns the description of an endpoint configuration created using the ` CreateEndpointConfig https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpointConfig.html`__ API.
ClientConfig (dict) --
Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.
MaxConcurrentInvocationsPerInstance (integer) --
The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.
OutputConfig (dict) --
Specifies the configuration for asynchronous inference invocation outputs.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3.
S3OutputPath (string) --
The Amazon S3 location to upload inference responses to.
NotificationConfig (dict) --
Specifies the configuration for notifications of inference results for asynchronous inference.
SuccessTopic (string) --
Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.
ErrorTopic (string) --
Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.
IncludeInferenceResponseIn (list) --
The Amazon SNS topics where you want the inference response to be included.
Note
The inference response is included only if the response size is less than or equal to 128 KB.
(string) --
S3FailurePath (string) --
The Amazon S3 location to upload failure inference responses to.
ExplainerConfig (dict) --
The configuration parameters for an explainer.
ClarifyExplainerConfig (dict) --
A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.
EnableExplanations (string) --
A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See ` EnableExplanations https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-online-explainability-create-endpoint.html#clarify-online-explainability-create-endpoint-enable`__ for additional information.
InferenceConfig (dict) --
The inference configuration parameter for the model container.
FeaturesAttribute (string) --
Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures' , it extracts a list of features [1,2,3] from request data '{"myfeatures":[1,2,3]}' .
ContentTemplate (string) --
A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features [1,2,3] into the record string '{"myfeatures":[1,2,3]}' . Required only when the model container input is in JSON Lines format.
MaxRecordCount (integer) --
The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset . A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1 , the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.
MaxPayloadInMB (integer) --
The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.
ProbabilityIndex (integer) --
A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.
Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6' , set ProbabilityIndex to 1 to select the probability value 0.6 .
Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3] .
LabelIndex (integer) --
A zero-based index used to extract a label header or list of label headers from model container output in CSV format.
Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"' , set LabelIndex to 0 to select the label headers ['cat','dog','fish'] .
ProbabilityAttribute (string) --
A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.
Example : If the model container output of a single request is '{"predicted_label":1,"probability":0.6}' , then set ProbabilityAttribute to 'probability' .
LabelAttribute (string) --
A JMESPath expression used to locate the list of label headers in the model container output.
Example : If the model container output of a batch request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}' , then set LabelAttribute to 'labels' to extract the list of label headers ["cat","dog","fish"]
LabelHeaders (list) --
For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.
(string) --
FeatureHeaders (list) --
The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.
(string) --
FeatureTypes (list) --
A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, ['text'] ). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.
(string) --
ShapConfig (dict) --
The configuration for SHAP analysis.
ShapBaselineConfig (dict) --
The configuration for the SHAP baseline of the Kernal SHAP algorithm.
MimeType (string) --
The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines' . Defaults to 'text/csv' .
ShapBaseline (string) --
The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.
ShapBaselineUri (string) --
The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud .
NumberOfSamples (integer) --
The number of samples to be used for analysis by the Kernal SHAP algorithm.
Note
The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint .
UseLogit (boolean) --
A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.
Seed (integer) --
The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.
TextConfig (dict) --
A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.
Language (string) --
Specifies the language of the text features in `ISO 639-1 < https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes>`__ or ISO 639-3 code of a supported language.
Note
For a mix of multiple languages, use code 'xx' .
Granularity (string) --
The unit of granularity for the analysis of text features. For example, if the unit is 'token' , then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.
ShadowProductionVariants (list) --
An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants .
(dict) --
Identifies a model that you want to host and the resources chosen to deploy for hosting it. If you are deploying multiple models, tell SageMaker how to distribute traffic among the models by specifying variant weights. For more information on production variants, check Production variants .
VariantName (string) --
The name of the production variant.
ModelName (string) --
The name of the model that you want to host. This is the name that you specified when creating the model.
InitialInstanceCount (integer) --
Number of instances to launch initially.
InstanceType (string) --
The ML compute instance type.
InitialVariantWeight (float) --
Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.
AcceleratorType (string) --
The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker .
CoreDumpConfig (dict) --
Specifies configuration for a core dump from the model container when the process crashes.
DestinationS3Uri (string) --
The Amazon S3 bucket to send the core dump to.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig . If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms" . For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
ServerlessConfig (dict) --
The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.
ModelDataDownloadTimeoutInSeconds (integer) --
The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.
ContainerStartupHealthCheckTimeoutInSeconds (integer) --
The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests .
EnableSSMAccess (boolean) --
You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint .
ManagedInstanceScaling (dict) --
Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status (string) --
Indicates whether managed instance scaling is enabled.
MinInstanceCount (integer) --
The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount (integer) --
The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig (dict) --
Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy (string) --
Sets how the endpoint routes incoming traffic:
LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.
RANDOM : The endpoint routes each request to a randomly chosen instance.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role that you assigned to the endpoint configuration.
VpcConfig (dict) --
Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
EnableNetworkIsolation (boolean) --
Indicates whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.
{'OfflineStoreConfig': {'TableFormat': {'Default'}}}
Use this operation to describe a FeatureGroup . The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup , and more.
See also: AWS API Documentation
Request Syntax
client.describe_feature_group(
FeatureGroupName='string',
NextToken='string'
)
string
[REQUIRED]
The name or Amazon Resource Name (ARN) of the FeatureGroup you want described.
string
A token to resume pagination of the list of Features (FeatureDefinitions ). 2,500 Features are returned by default.
dict
Response Syntax
{
'FeatureGroupArn': 'string',
'FeatureGroupName': 'string',
'RecordIdentifierFeatureName': 'string',
'EventTimeFeatureName': 'string',
'FeatureDefinitions': [
{
'FeatureName': 'string',
'FeatureType': 'Integral'|'Fractional'|'String',
'CollectionType': 'List'|'Set'|'Vector',
'CollectionConfig': {
'VectorConfig': {
'Dimension': 123
}
}
},
],
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'OnlineStoreConfig': {
'SecurityConfig': {
'KmsKeyId': 'string'
},
'EnableOnlineStore': True|False,
'TtlDuration': {
'Unit': 'Seconds'|'Minutes'|'Hours'|'Days'|'Weeks',
'Value': 123
},
'StorageType': 'Standard'|'InMemory'
},
'OfflineStoreConfig': {
'S3StorageConfig': {
'S3Uri': 'string',
'KmsKeyId': 'string',
'ResolvedOutputS3Uri': 'string'
},
'DisableGlueTableCreation': True|False,
'DataCatalogConfig': {
'TableName': 'string',
'Catalog': 'string',
'Database': 'string'
},
'TableFormat': 'Default'|'Glue'|'Iceberg'
},
'RoleArn': 'string',
'FeatureGroupStatus': 'Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
'OfflineStoreStatus': {
'Status': 'Active'|'Blocked'|'Disabled',
'BlockedReason': 'string'
},
'LastUpdateStatus': {
'Status': 'Successful'|'Failed'|'InProgress',
'FailureReason': 'string'
},
'FailureReason': 'string',
'Description': 'string',
'NextToken': 'string',
'OnlineStoreTotalSizeBytes': 123
}
Response Structure
(dict) --
FeatureGroupArn (string) --
The Amazon Resource Name (ARN) of the FeatureGroup .
FeatureGroupName (string) --
he name of the FeatureGroup .
RecordIdentifierFeatureName (string) --
The name of the Feature used for RecordIdentifier , whose value uniquely identifies a record stored in the feature store.
EventTimeFeatureName (string) --
The name of the feature that stores the EventTime of a Record in a FeatureGroup .
An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup . All Records in the FeatureGroup have a corresponding EventTime .
FeatureDefinitions (list) --
A list of the Features in the FeatureGroup . Each feature is defined by a FeatureName and FeatureType .
(dict) --
A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .
FeatureName (string) --
The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .
FeatureType (string) --
The value type of a feature. Valid values are Integral, Fractional, or String.
CollectionType (string) --
A grouping of elements where each element within the collection must have the same feature type (String , Integral , or Fractional ).
List : An ordered collection of elements.
Set : An unordered collection of unique elements.
Vector : A specialized list that represents a fixed-size array of elements. The vector dimension is determined by you. Must have elements with fractional feature types.
CollectionConfig (dict) --
Configuration for your collection.
VectorConfig (dict) --
Configuration for your vector collection type.
Dimension : The number of elements in your vector.
Dimension (integer) --
The number of elements in your vector.
CreationTime (datetime) --
A timestamp indicating when SageMaker created the FeatureGroup .
LastModifiedTime (datetime) --
A timestamp indicating when the feature group was last updated.
OnlineStoreConfig (dict) --
The configuration for the OnlineStore .
SecurityConfig (dict) --
Use to specify KMS Key ID (KMSKeyId ) for at-rest encryption of your OnlineStore .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key ARN that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId :
"kms:Encrypt"
"kms:Decrypt"
"kms:DescribeKey"
"kms:CreateGrant"
"kms:RetireGrant"
"kms:ReEncryptFrom"
"kms:ReEncryptTo"
"kms:GenerateDataKey"
"kms:ListAliases"
"kms:ListGrants"
"kms:RevokeGrant"
The caller (either user or IAM role) to all DataPlane operations (PutRecord , GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId :
"kms:Decrypt"
EnableOnlineStore (boolean) --
Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.
The default value is False .
TtlDuration (dict) --
Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration . For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.
Unit (string) --
TtlDuration time unit.
Value (integer) --
TtlDuration time value.
StorageType (string) --
Option for different tiers of low latency storage for real-time data retrieval.
Standard : A managed low latency data store for feature groups.
InMemory : A managed data store for feature groups that supports very low latency retrieval.
OfflineStoreConfig (dict) --
The configuration of the offline store. It includes the following configurations:
Amazon S3 location of the offline store.
Configuration of the Glue data catalog.
Table format of the offline store.
Option to disable the automatic creation of a Glue table for the offline store.
Encryption configuration.
S3StorageConfig (dict) --
The Amazon Simple Storage (Amazon S3) location of OfflineStore .
S3Uri (string) --
The S3 URI, or location in Amazon S3, of OfflineStore .
S3 URIs have a format similar to the following: s3://example-bucket/prefix/ .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key ARN of the key used to encrypt any objects written into the OfflineStore S3 location.
The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId :
"kms:GenerateDataKey"
ResolvedOutputS3Uri (string) --
The S3 path where offline records are written.
DisableGlueTableCreation (boolean) --
Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore . If set to False , Feature Store will name the OfflineStore Glue table following Athena's naming recommendations .
The default value is False .
DataCatalogConfig (dict) --
The meta data of the Glue table that is autogenerated when an OfflineStore is created.
TableName (string) --
The name of the Glue table.
Catalog (string) --
The name of the Glue table catalog.
Database (string) --
The name of the Glue table database.
TableFormat (string) --
Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg .
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
FeatureGroupStatus (string) --
The status of the feature group.
OfflineStoreStatus (dict) --
The status of the OfflineStore . Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked
Status (string) --
An OfflineStore status.
BlockedReason (string) --
The justification for why the OfflineStoreStatus is Blocked (if applicable).
LastUpdateStatus (dict) --
A value indicating whether the update made to the feature group was successful.
Status (string) --
A value that indicates whether the update was made successful.
FailureReason (string) --
If the update wasn't successful, indicates the reason why it failed.
FailureReason (string) --
The reason that the FeatureGroup failed to be replicated in the OfflineStore . This is failure can occur because:
The FeatureGroup could not be created in the OfflineStore .
The FeatureGroup could not be deleted from the OfflineStore .
Description (string) --
A free form description of the feature group.
NextToken (string) --
A token to resume pagination of the list of Features (FeatureDefinitions ).
OnlineStoreTotalSizeBytes (integer) --
The size of the OnlineStore in bytes.
{'HumanLoopRequestSource': {'AwsManagedHumanLoopRequestSource': {'AWS/Bedrock/ModelEvaluation',
'AWS/Handshake/VerifyIdentity',
'AWS/Textract/AnalyzeExpense'}}}
Returns information about the specified flow definition.
See also: AWS API Documentation
Request Syntax
client.describe_flow_definition(
FlowDefinitionName='string'
)
string
[REQUIRED]
The name of the flow definition.
dict
Response Syntax
{
'FlowDefinitionArn': 'string',
'FlowDefinitionName': 'string',
'FlowDefinitionStatus': 'Initializing'|'Active'|'Failed'|'Deleting',
'CreationTime': datetime(2015, 1, 1),
'HumanLoopRequestSource': {
'AwsManagedHumanLoopRequestSource': 'AWS/Rekognition/DetectModerationLabels/Image/V3'|'AWS/Textract/AnalyzeDocument/Forms/V1'|'AWS/Textract/AnalyzeExpense'|'AWS/Handshake/VerifyIdentity'|'AWS/Bedrock/ModelEvaluation'
},
'HumanLoopActivationConfig': {
'HumanLoopActivationConditionsConfig': {
'HumanLoopActivationConditions': 'string'
}
},
'HumanLoopConfig': {
'WorkteamArn': 'string',
'HumanTaskUiArn': 'string',
'TaskTitle': 'string',
'TaskDescription': 'string',
'TaskCount': 123,
'TaskAvailabilityLifetimeInSeconds': 123,
'TaskTimeLimitInSeconds': 123,
'TaskKeywords': [
'string',
],
'PublicWorkforceTaskPrice': {
'AmountInUsd': {
'Dollars': 123,
'Cents': 123,
'TenthFractionsOfACent': 123
}
}
},
'OutputConfig': {
'S3OutputPath': 'string',
'KmsKeyId': 'string'
},
'RoleArn': 'string',
'FailureReason': 'string'
}
Response Structure
(dict) --
FlowDefinitionArn (string) --
The Amazon Resource Name (ARN) of the flow defintion.
FlowDefinitionName (string) --
The Amazon Resource Name (ARN) of the flow definition.
FlowDefinitionStatus (string) --
The status of the flow definition. Valid values are listed below.
CreationTime (datetime) --
The timestamp when the flow definition was created.
HumanLoopRequestSource (dict) --
Container for configuring the source of human task requests. Used to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
AwsManagedHumanLoopRequestSource (string) --
Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:
HumanLoopActivationConfig (dict) --
An object containing information about what triggers a human review workflow.
HumanLoopActivationConditionsConfig (dict) --
Container structure for defining under what conditions SageMaker creates a human loop.
HumanLoopActivationConditions (string) --
JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide .
HumanLoopConfig (dict) --
An object containing information about who works on the task, the workforce task price, and other task details.
WorkteamArn (string) --
Amazon Resource Name (ARN) of a team of workers. To learn more about the types of workforces and work teams you can create and use with Amazon A2I, see Create and Manage Workforces .
HumanTaskUiArn (string) --
The Amazon Resource Name (ARN) of the human task user interface.
You can use standard HTML and Crowd HTML Elements to create a custom worker task template. You use this template to create a human task UI.
To learn how to create a custom HTML template, see Create Custom Worker Task Template .
To learn how to create a human task UI, which is a worker task template that can be used in a flow definition, see Create and Delete a Worker Task Templates .
TaskTitle (string) --
A title for the human worker task.
TaskDescription (string) --
A description for the human worker task.
TaskCount (integer) --
The number of distinct workers who will perform the same task on each object. For example, if TaskCount is set to 3 for an image classification labeling job, three workers will classify each input image. Increasing TaskCount can improve label accuracy.
TaskAvailabilityLifetimeInSeconds (integer) --
The length of time that a task remains available for review by human workers.
TaskTimeLimitInSeconds (integer) --
The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).
TaskKeywords (list) --
Keywords used to describe the task so that workers can discover the task.
(string) --
PublicWorkforceTaskPrice (dict) --
Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed.
Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer.
0.036
0.048
0.060
0.072
0.120
0.240
0.360
0.480
0.600
0.720
0.840
0.960
1.080
1.200
Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars.
0.012
0.024
0.036
0.048
0.060
0.072
0.120
0.240
0.360
0.480
0.600
0.720
0.840
0.960
1.080
1.200
Use one of the following prices for semantic segmentation tasks. Prices are in US dollars.
0.840
0.960
1.080
1.200
Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars.
2.400
2.280
2.160
2.040
1.920
1.800
1.680
1.560
1.440
1.320
1.200
1.080
0.960
0.840
0.720
0.600
0.480
0.360
0.240
0.120
0.072
0.060
0.048
0.036
0.024
0.012
Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars.
1.200
1.080
0.960
0.840
0.720
0.600
0.480
0.360
0.240
0.120
0.072
0.060
0.048
0.036
0.024
0.012
Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars.
1.200
1.080
0.960
0.840
0.720
0.600
0.480
0.360
0.240
0.120
0.072
0.060
0.048
0.036
0.024
0.012
AmountInUsd (dict) --
Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
Dollars (integer) --
The whole number of dollars in the amount.
Cents (integer) --
The fractional portion, in cents, of the amount.
TenthFractionsOfACent (integer) --
Fractions of a cent, in tenths.
OutputConfig (dict) --
An object containing information about the output file.
S3OutputPath (string) --
The Amazon S3 path where the object containing human output will be made available.
To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data .
KmsKeyId (string) --
The Amazon Key Management Service (KMS) key ID for server-side encryption.
RoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) execution role for the flow definition.
FailureReason (string) --
The reason your flow definition failed.
{'HyperParameterTuningJobStatus': {'DeleteFailed', 'Deleting'},
'TrainingJobDefinition': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}}},
'TrainingJobDefinitions': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}}}}
Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include the name, Amazon Resource Name (ARN), job status of your tuning job and more.
See also: AWS API Documentation
Request Syntax
client.describe_hyper_parameter_tuning_job(
HyperParameterTuningJobName='string'
)
string
[REQUIRED]
The name of the tuning job.
dict
Response Syntax
{
'HyperParameterTuningJobName': 'string',
'HyperParameterTuningJobArn': 'string',
'HyperParameterTuningJobConfig': {
'Strategy': 'Bayesian'|'Random'|'Hyperband'|'Grid',
'StrategyConfig': {
'HyperbandStrategyConfig': {
'MinResource': 123,
'MaxResource': 123
}
},
'HyperParameterTuningJobObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'ResourceLimits': {
'MaxNumberOfTrainingJobs': 123,
'MaxParallelTrainingJobs': 123,
'MaxRuntimeInSeconds': 123
},
'ParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'TrainingJobEarlyStoppingType': 'Off'|'Auto',
'TuningJobCompletionCriteria': {
'TargetObjectiveMetricValue': ...,
'BestObjectiveNotImproving': {
'MaxNumberOfTrainingJobsNotImproving': 123
},
'ConvergenceDetected': {
'CompleteOnConvergence': 'Disabled'|'Enabled'
}
},
'RandomSeed': 123
},
'TrainingJobDefinition': {
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'AlgorithmName': 'string',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'KeepAlivePeriodInSeconds': 123,
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
]
},
'HyperParameterTuningResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'AllocationStrategy': 'Prioritized',
'InstanceConfigs': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123
},
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
},
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'Environment': {
'string': 'string'
}
},
'TrainingJobDefinitions': [
{
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'AlgorithmName': 'string',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'KeepAlivePeriodInSeconds': 123,
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
]
},
'HyperParameterTuningResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'AllocationStrategy': 'Prioritized',
'InstanceConfigs': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123
},
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
},
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'Environment': {
'string': 'string'
}
},
],
'HyperParameterTuningJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping'|'Deleting'|'DeleteFailed',
'CreationTime': datetime(2015, 1, 1),
'HyperParameterTuningEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'TrainingJobStatusCounters': {
'Completed': 123,
'InProgress': 123,
'RetryableError': 123,
'NonRetryableError': 123,
'Stopped': 123
},
'ObjectiveStatusCounters': {
'Succeeded': 123,
'Pending': 123,
'Failed': 123
},
'BestTrainingJob': {
'TrainingJobDefinitionName': 'string',
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobName': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'TunedHyperParameters': {
'string': 'string'
},
'FailureReason': 'string',
'FinalHyperParameterTuningJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string',
'Value': ...
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed'
},
'OverallBestTrainingJob': {
'TrainingJobDefinitionName': 'string',
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobName': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'TunedHyperParameters': {
'string': 'string'
},
'FailureReason': 'string',
'FinalHyperParameterTuningJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string',
'Value': ...
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed'
},
'WarmStartConfig': {
'ParentHyperParameterTuningJobs': [
{
'HyperParameterTuningJobName': 'string'
},
],
'WarmStartType': 'IdenticalDataAndAlgorithm'|'TransferLearning'
},
'Autotune': {
'Mode': 'Enabled'
},
'FailureReason': 'string',
'TuningJobCompletionDetails': {
'NumberOfTrainingJobsObjectiveNotImproving': 123,
'ConvergenceDetectedTime': datetime(2015, 1, 1)
},
'ConsumedResources': {
'RuntimeInSeconds': 123
}
}
Response Structure
(dict) --
HyperParameterTuningJobName (string) --
The name of the hyperparameter tuning job.
HyperParameterTuningJobArn (string) --
The Amazon Resource Name (ARN) of the tuning job.
HyperParameterTuningJobConfig (dict) --
The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.
Strategy (string) --
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works .
StrategyConfig (dict) --
The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig .
HyperbandStrategyConfig (dict) --
The configuration for the object that specifies the Hyperband strategy. This parameter is only supported for the Hyperband selection for Strategy within the HyperParameterTuningJobConfig API.
MinResource (integer) --
The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job is not stopped by Hyperband .
MaxResource (integer) --
The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTrainingJ attempts to infer MaxResource from the following keys (if present) in StaticsHyperParameters :
epochs
numepochs
n-epochs
n_epochs
num_epochs
If HyperbandStrategyConfig is unable to infer a value for MaxResource , it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions . For distributive training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.
HyperParameterTuningJobObjective (dict) --
The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
ResourceLimits (dict) --
The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
MaxNumberOfTrainingJobs (integer) --
The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs (integer) --
The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
MaxRuntimeInSeconds (integer) --
The maximum time in seconds that a hyperparameter tuning job can run.
ParameterRanges (dict) --
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) --
The name of the hyperparameter to search.
MinValue (string) --
The minimum value of the hyperparameter to search.
MaxValue (string) --
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) --
The name of the continuous hyperparameter to tune.
MinValue (string) --
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) --
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) --
The name of the categorical hyperparameter to tune.
Values (list) --
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) --
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) --
An example value of the hyperparameter to optimize using Autotune.
TrainingJobEarlyStoppingType (string) --
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband . This parameter can take on one of the following values (the default value is OFF ):
OFF
Training jobs launched by the hyperparameter tuning job do not use early stopping.
AUTO
SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early .
TuningJobCompletionCriteria (dict) --
The tuning job's completion criteria.
TargetObjectiveMetricValue (float) --
The value of the objective metric.
BestObjectiveNotImproving (dict) --
A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.
MaxNumberOfTrainingJobsNotImproving (integer) --
The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
ConvergenceDetected (dict) --
A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.
CompleteOnConvergence (string) --
A flag to stop a tuning job once AMT has detected that the job has converged.
RandomSeed (integer) --
A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.
TrainingJobDefinition (dict) --
The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.
DefinitionName (string) --
The job definition name.
TuningObjective (dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables .
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
HyperParameterRanges (dict) --
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) --
The name of the hyperparameter to search.
MinValue (string) --
The minimum value of the hyperparameter to search.
MaxValue (string) --
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) --
The name of the continuous hyperparameter to tune.
MinValue (string) --
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) --
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) --
The name of the categorical hyperparameter to tune.
Values (list) --
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) --
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) --
An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters (dict) --
Specifies the values of hyperparameters that do not change for the tuning job.
(string) --
(string) --
AlgorithmSpecification (dict) --
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
AlgorithmName (string) --
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
MetricDefinitions (list) --
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig (list) --
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
VpcConfig (dict) --
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
OutputDataConfig (dict) --
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
Note
If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
HyperParameterTuningResourceConfig (dict) --
The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
InstanceType (string) --
The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
InstanceCount (integer) --
The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.
VolumeSizeInGB (integer) --
The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.
Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .
Note
SageMaker supports only the General Purpose SSD (gp2) storage volume type.
VolumeKmsKeyId (string) --
A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
AllocationStrategy (string) --
The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
InstanceConfigs (list) --
A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.
Note
If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .
(dict) --
The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
InstanceType (string) --
The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .
InstanceCount (integer) --
The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
VolumeSizeInGB (integer) --
The volume size in GB of the data to be processed for hyperparameter optimization (optional).
StoppingCondition (dict) --
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
EnableNetworkIsolation (boolean) --
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Environment (dict) --
An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note
The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
(string) --
(string) --
TrainingJobDefinitions (list) --
A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
(dict) --
Defines the training jobs launched by a hyperparameter tuning job.
DefinitionName (string) --
The job definition name.
TuningObjective (dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables .
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
HyperParameterRanges (dict) --
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) --
The name of the hyperparameter to search.
MinValue (string) --
The minimum value of the hyperparameter to search.
MaxValue (string) --
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) --
The name of the continuous hyperparameter to tune.
MinValue (string) --
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) --
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) --
The name of the categorical hyperparameter to tune.
Values (list) --
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) --
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) --
An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters (dict) --
Specifies the values of hyperparameters that do not change for the tuning job.
(string) --
(string) --
AlgorithmSpecification (dict) --
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
AlgorithmName (string) --
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
MetricDefinitions (list) --
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig (list) --
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
VpcConfig (dict) --
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
OutputDataConfig (dict) --
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
Note
If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
HyperParameterTuningResourceConfig (dict) --
The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
InstanceType (string) --
The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
InstanceCount (integer) --
The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.
VolumeSizeInGB (integer) --
The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.
Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .
Note
SageMaker supports only the General Purpose SSD (gp2) storage volume type.
VolumeKmsKeyId (string) --
A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
AllocationStrategy (string) --
The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
InstanceConfigs (list) --
A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.
Note
If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .
(dict) --
The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
InstanceType (string) --
The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .
InstanceCount (integer) --
The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
VolumeSizeInGB (integer) --
The volume size in GB of the data to be processed for hyperparameter optimization (optional).
StoppingCondition (dict) --
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
EnableNetworkIsolation (boolean) --
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Environment (dict) --
An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note
The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
(string) --
(string) --
HyperParameterTuningJobStatus (string) --
The status of the tuning job: InProgress, Completed, Failed, Stopping, or Stopped.
CreationTime (datetime) --
The date and time that the tuning job started.
HyperParameterTuningEndTime (datetime) --
The date and time that the tuning job ended.
LastModifiedTime (datetime) --
The date and time that the status of the tuning job was modified.
TrainingJobStatusCounters (dict) --
The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.
Completed (integer) --
The number of completed training jobs launched by the hyperparameter tuning job.
InProgress (integer) --
The number of in-progress training jobs launched by a hyperparameter tuning job.
RetryableError (integer) --
The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
NonRetryableError (integer) --
The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
Stopped (integer) --
The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
ObjectiveStatusCounters (dict) --
The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.
Succeeded (integer) --
The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending (integer) --
The number of training jobs that are in progress and pending evaluation of their final objective metric.
Failed (integer) --
The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
BestTrainingJob (dict) --
A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective .
TrainingJobDefinitionName (string) --
The training job definition name.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobName (string) --
The HyperParameter tuning job that launched the training job.
CreationTime (datetime) --
The date and time that the training job was created.
TrainingStartTime (datetime) --
The date and time that the training job started.
TrainingEndTime (datetime) --
Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
TrainingJobStatus (string) --
The status of the training job.
TunedHyperParameters (dict) --
A list of the hyperparameters for which you specified ranges to search.
(string) --
(string) --
FailureReason (string) --
The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric (dict) --
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type (string) --
Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
MetricName (string) --
The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables .
Value (float) --
The value of the objective metric.
ObjectiveStatus (string) --
The status of the objective metric for the training job:
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending: The training job is in progress and evaluation of its final objective metric is pending.
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
OverallBestTrainingJob (dict) --
If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM , this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.
TrainingJobDefinitionName (string) --
The training job definition name.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobName (string) --
The HyperParameter tuning job that launched the training job.
CreationTime (datetime) --
The date and time that the training job was created.
TrainingStartTime (datetime) --
The date and time that the training job started.
TrainingEndTime (datetime) --
Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
TrainingJobStatus (string) --
The status of the training job.
TunedHyperParameters (dict) --
A list of the hyperparameters for which you specified ranges to search.
(string) --
(string) --
FailureReason (string) --
The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric (dict) --
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type (string) --
Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
MetricName (string) --
The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables .
Value (float) --
The value of the objective metric.
ObjectiveStatus (string) --
The status of the objective metric for the training job:
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending: The training job is in progress and evaluation of its final objective metric is pending.
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
WarmStartConfig (dict) --
The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
ParentHyperParameterTuningJobs (list) --
An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point .
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
(dict) --
A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
HyperParameterTuningJobName (string) --
The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
WarmStartType (string) --
Specifies one of the following:
IDENTICAL_DATA_AND_ALGORITHM
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
TRANSFER_LEARNING
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
Autotune (dict) --
A flag to indicate if autotune is enabled for the hyperparameter tuning job.
Mode (string) --
Set Mode to Enabled if you want to use Autotune.
FailureReason (string) --
If the tuning job failed, the reason it failed.
TuningJobCompletionDetails (dict) --
Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function.
NumberOfTrainingJobsObjectiveNotImproving (integer) --
The number of training jobs launched by a tuning job that are not improving (1% or less) as measured by model performance evaluated against an objective function.
ConvergenceDetectedTime (datetime) --
The time in timestamp format that AMT detected model convergence, as defined by a lack of significant improvement over time based on criteria developed over a wide range of diverse benchmarking tests.
ConsumedResources (dict) --
The total resources consumed by your hyperparameter tuning job.
RuntimeInSeconds (integer) --
The wall clock runtime in seconds used by your hyperparameter tuning job.
{'InferenceRecommendations': {'EndpointConfiguration': {'InstanceType': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}},
'InputConfig': {'EndpointConfigurations': {'InstanceType': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}},
'Status': {'DELETING', 'DELETED'}}
Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.
See also: AWS API Documentation
Request Syntax
client.describe_inference_recommendations_job(
JobName='string'
)
string
[REQUIRED]
The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
dict
Response Syntax
{
'JobName': 'string',
'JobDescription': 'string',
'JobType': 'Default'|'Advanced',
'JobArn': 'string',
'RoleArn': 'string',
'Status': 'PENDING'|'IN_PROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'DELETED',
'CreationTime': datetime(2015, 1, 1),
'CompletionTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'InputConfig': {
'ModelPackageVersionArn': 'string',
'ModelName': 'string',
'JobDurationInSeconds': 123,
'TrafficPattern': {
'TrafficType': 'PHASES'|'STAIRS',
'Phases': [
{
'InitialNumberOfUsers': 123,
'SpawnRate': 123,
'DurationInSeconds': 123
},
],
'Stairs': {
'DurationInSeconds': 123,
'NumberOfSteps': 123,
'UsersPerStep': 123
}
},
'ResourceLimit': {
'MaxNumberOfTests': 123,
'MaxParallelOfTests': 123
},
'EndpointConfigurations': [
{
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
'ServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'InferenceSpecificationName': 'string',
'EnvironmentParameterRanges': {
'CategoricalParameterRanges': [
{
'Name': 'string',
'Value': [
'string',
]
},
]
}
},
],
'VolumeKmsKeyId': 'string',
'ContainerConfig': {
'Domain': 'string',
'Task': 'string',
'Framework': 'string',
'FrameworkVersion': 'string',
'PayloadConfig': {
'SamplePayloadUrl': 'string',
'SupportedContentTypes': [
'string',
]
},
'NearestModelName': 'string',
'SupportedInstanceTypes': [
'string',
],
'SupportedEndpointType': 'RealTime'|'Serverless',
'DataInputConfig': 'string',
'SupportedResponseMIMETypes': [
'string',
]
},
'Endpoints': [
{
'EndpointName': 'string'
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'StoppingConditions': {
'MaxInvocations': 123,
'ModelLatencyThresholds': [
{
'Percentile': 'string',
'ValueInMilliseconds': 123
},
],
'FlatInvocations': 'Continue'|'Stop'
},
'InferenceRecommendations': [
{
'RecommendationId': 'string',
'Metrics': {
'CostPerHour': ...,
'CostPerInference': ...,
'MaxInvocations': 123,
'ModelLatency': 123,
'CpuUtilization': ...,
'MemoryUtilization': ...,
'ModelSetupTime': 123
},
'EndpointConfiguration': {
'EndpointName': 'string',
'VariantName': 'string',
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
'InitialInstanceCount': 123,
'ServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
}
},
'ModelConfiguration': {
'InferenceSpecificationName': 'string',
'EnvironmentParameters': [
{
'Key': 'string',
'ValueType': 'string',
'Value': 'string'
},
],
'CompilationJobName': 'string'
},
'InvocationEndTime': datetime(2015, 1, 1),
'InvocationStartTime': datetime(2015, 1, 1)
},
],
'EndpointPerformances': [
{
'Metrics': {
'MaxInvocations': 123,
'ModelLatency': 123
},
'EndpointInfo': {
'EndpointName': 'string'
}
},
]
}
Response Structure
(dict) --
JobName (string) --
The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
JobDescription (string) --
The job description that you provided when you initiated the job.
JobType (string) --
The job type that you provided when you initiated the job.
JobArn (string) --
The Amazon Resource Name (ARN) of the job.
RoleArn (string) --
The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role you provided when you initiated the job.
Status (string) --
The status of the job.
CreationTime (datetime) --
A timestamp that shows when the job was created.
CompletionTime (datetime) --
A timestamp that shows when the job completed.
LastModifiedTime (datetime) --
A timestamp that shows when the job was last modified.
FailureReason (string) --
If the job fails, provides information why the job failed.
InputConfig (dict) --
Returns information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations you provided when you initiated the job.
ModelPackageVersionArn (string) --
The Amazon Resource Name (ARN) of a versioned model package.
ModelName (string) --
The name of the created model.
JobDurationInSeconds (integer) --
Specifies the maximum duration of the job, in seconds. The maximum value is 18,000 seconds.
TrafficPattern (dict) --
Specifies the traffic pattern of the job.
TrafficType (string) --
Defines the traffic patterns. Choose either PHASES or STAIRS .
Phases (list) --
Defines the phases traffic specification.
(dict) --
Defines the traffic pattern.
InitialNumberOfUsers (integer) --
Specifies how many concurrent users to start with. The value should be between 1 and 3.
SpawnRate (integer) --
Specified how many new users to spawn in a minute.
DurationInSeconds (integer) --
Specifies how long a traffic phase should be. For custom load tests, the value should be between 120 and 3600. This value should not exceed JobDurationInSeconds .
Stairs (dict) --
Defines the stairs traffic pattern.
DurationInSeconds (integer) --
Defines how long each traffic step should be.
NumberOfSteps (integer) --
Specifies how many steps to perform during traffic.
UsersPerStep (integer) --
Specifies how many new users to spawn in each step.
ResourceLimit (dict) --
Defines the resource limit of the job.
MaxNumberOfTests (integer) --
Defines the maximum number of load tests.
MaxParallelOfTests (integer) --
Defines the maximum number of parallel load tests.
EndpointConfigurations (list) --
Specifies the endpoint configuration to use for a job.
(dict) --
The endpoint configuration for the load test.
InstanceType (string) --
The instance types to use for the load test.
ServerlessConfig (dict) --
Specifies the serverless configuration for an endpoint variant.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
InferenceSpecificationName (string) --
The inference specification name in the model package version.
EnvironmentParameterRanges (dict) --
The parameter you want to benchmark against.
CategoricalParameterRanges (list) --
Specified a list of parameters for each category.
(dict) --
Environment parameters you want to benchmark your load test against.
Name (string) --
The Name of the environment variable.
Value (list) --
The list of values you can pass.
(string) --
VolumeKmsKeyId (string) --
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. This key will be passed to SageMaker Hosting for endpoint creation.
The SageMaker execution role must have kms:CreateGrant permission in order to encrypt data on the storage volume of the endpoints created for inference recommendation. The inference recommendation job will fail asynchronously during endpoint configuration creation if the role passed does not have kms:CreateGrant permission.
The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:<region>:<account>:key/<key-id-12ab-34cd-56ef-1234567890ab>"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:<region>:<account>:alias/<ExampleAlias>"
For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
ContainerConfig (dict) --
Specifies mandatory fields for running an Inference Recommender job. The fields specified in ContainerConfig override the corresponding fields in the model package.
Domain (string) --
The machine learning domain of the model and its components.
Valid Values: COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING
Task (string) --
The machine learning task that the model accomplishes.
Valid Values: IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER
Framework (string) --
The machine learning framework of the container image.
Valid Values: TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN
FrameworkVersion (string) --
The framework version of the container image.
PayloadConfig (dict) --
Specifies the SamplePayloadUrl and all other sample payload-related fields.
SamplePayloadUrl (string) --
The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
NearestModelName (string) --
The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model.
Valid Values: efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet
SupportedInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
(string) --
SupportedEndpointType (string) --
The endpoint type to receive recommendations for. By default this is null, and the results of the inference recommendation job return a combined list of both real-time and serverless benchmarks. By specifying a value for this field, you can receive a longer list of benchmarks for the desired endpoint type.
DataInputConfig (string) --
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. This field is used for optimizing your model using SageMaker Neo. For more information, see DataInputConfig .
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
Endpoints (list) --
Existing customer endpoints on which to run an Inference Recommender job.
(dict) --
Details about a customer endpoint that was compared in an Inference Recommender job.
EndpointName (string) --
The name of a customer's endpoint.
VpcConfig (dict) --
Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.
SecurityGroupIds (list) --
The VPC security group IDs. IDs have the form of sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your model.
(string) --
StoppingConditions (dict) --
The stopping conditions that you provided when you initiated the job.
MaxInvocations (integer) --
The maximum number of requests per minute expected for the endpoint.
ModelLatencyThresholds (list) --
The interval of time taken by a model to respond as viewed from SageMaker. The interval includes the local communication time taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container.
(dict) --
The model latency threshold.
Percentile (string) --
The model latency percentile threshold. Acceptable values are P95 and P99 . For custom load tests, specify the value as P95 .
ValueInMilliseconds (integer) --
The model latency percentile value in milliseconds.
FlatInvocations (string) --
Stops a load test when the number of invocations (TPS) peaks and flattens, which means that the instance has reached capacity. The default value is Stop . If you want the load test to continue after invocations have flattened, set the value to Continue .
InferenceRecommendations (list) --
The recommendations made by Inference Recommender.
(dict) --
A list of recommendations made by Amazon SageMaker Inference Recommender.
RecommendationId (string) --
The recommendation ID which uniquely identifies each recommendation.
Metrics (dict) --
The metrics used to decide what recommendation to make.
CostPerHour (float) --
Defines the cost per hour for the instance.
CostPerInference (float) --
Defines the cost per inference for the instance .
MaxInvocations (integer) --
The expected maximum number of requests per minute for the instance.
ModelLatency (integer) --
The expected model latency at maximum invocation per minute for the instance.
CpuUtilization (float) --
The expected CPU utilization at maximum invocations per minute for the instance.
NaN indicates that the value is not available.
MemoryUtilization (float) --
The expected memory utilization at maximum invocations per minute for the instance.
NaN indicates that the value is not available.
ModelSetupTime (integer) --
The time it takes to launch new compute resources for a serverless endpoint. The time can vary depending on the model size, how long it takes to download the model, and the start-up time of the container.
NaN indicates that the value is not available.
EndpointConfiguration (dict) --
Defines the endpoint configuration parameters.
EndpointName (string) --
The name of the endpoint made during a recommendation job.
VariantName (string) --
The name of the production variant (deployed model) made during a recommendation job.
InstanceType (string) --
The instance type recommended by Amazon SageMaker Inference Recommender.
InitialInstanceCount (integer) --
The number of instances recommended to launch initially.
ServerlessConfig (dict) --
Specifies the serverless configuration for an endpoint variant.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
ModelConfiguration (dict) --
Defines the model configuration.
InferenceSpecificationName (string) --
The inference specification name in the model package version.
EnvironmentParameters (list) --
Defines the environment parameters that includes key, value types, and values.
(dict) --
A list of environment parameters suggested by the Amazon SageMaker Inference Recommender.
Key (string) --
The environment key suggested by the Amazon SageMaker Inference Recommender.
ValueType (string) --
The value type suggested by the Amazon SageMaker Inference Recommender.
Value (string) --
The value suggested by the Amazon SageMaker Inference Recommender.
CompilationJobName (string) --
The name of the compilation job used to create the recommended model artifacts.
InvocationEndTime (datetime) --
A timestamp that shows when the benchmark completed.
InvocationStartTime (datetime) --
A timestamp that shows when the benchmark started.
EndpointPerformances (list) --
The performance results from running an Inference Recommender job on an existing endpoint.
(dict) --
The performance results from running an Inference Recommender job on an existing endpoint.
Metrics (dict) --
The metrics for an existing endpoint.
MaxInvocations (integer) --
The expected maximum number of requests per minute for the instance.
ModelLatency (integer) --
The expected model latency at maximum invocations per minute for the instance.
EndpointInfo (dict) --
Details about a customer endpoint that was compared in an Inference Recommender job.
EndpointName (string) --
The name of a customer's endpoint.
{'DeploymentRecommendation': {'RealTimeInferenceRecommendations': {'InstanceType': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}}}
Describes a model that you created using the CreateModel API.
See also: AWS API Documentation
Request Syntax
client.describe_model(
ModelName='string'
)
string
[REQUIRED]
The name of the model.
dict
Response Syntax
{
'ModelName': 'string',
'PrimaryContainer': {
'ContainerHostname': 'string',
'Image': 'string',
'ImageConfig': {
'RepositoryAccessMode': 'Platform'|'Vpc',
'RepositoryAuthConfig': {
'RepositoryCredentialsProviderArn': 'string'
}
},
'Mode': 'SingleModel'|'MultiModel',
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
}
}
},
'Environment': {
'string': 'string'
},
'ModelPackageName': 'string',
'InferenceSpecificationName': 'string',
'MultiModelConfig': {
'ModelCacheSetting': 'Enabled'|'Disabled'
}
},
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageConfig': {
'RepositoryAccessMode': 'Platform'|'Vpc',
'RepositoryAuthConfig': {
'RepositoryCredentialsProviderArn': 'string'
}
},
'Mode': 'SingleModel'|'MultiModel',
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
}
}
},
'Environment': {
'string': 'string'
},
'ModelPackageName': 'string',
'InferenceSpecificationName': 'string',
'MultiModelConfig': {
'ModelCacheSetting': 'Enabled'|'Disabled'
}
},
],
'InferenceExecutionConfig': {
'Mode': 'Serial'|'Direct'
},
'ExecutionRoleArn': 'string',
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'CreationTime': datetime(2015, 1, 1),
'ModelArn': 'string',
'EnableNetworkIsolation': True|False,
'DeploymentRecommendation': {
'RecommendationStatus': 'IN_PROGRESS'|'COMPLETED'|'FAILED'|'NOT_APPLICABLE',
'RealTimeInferenceRecommendations': [
{
'RecommendationId': 'string',
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
'Environment': {
'string': 'string'
}
},
]
}
}
Response Structure
(dict) --
ModelName (string) --
Name of the SageMaker model.
PrimaryContainer (dict) --
The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.
ContainerHostname (string) --
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline . If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
Image (string) --
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
ImageConfig (dict) --
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers .
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
RepositoryAccessMode (string) --
Set this to one of the following values:
Platform - The model image is hosted in Amazon ECR.
Vpc - The model image is hosted in a private Docker registry in your VPC.
RepositoryAuthConfig (dict) --
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
RepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Mode (string) --
Whether the container hosts a single model or multiple models.
ModelDataUrl (string) --
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters .
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
ModelDataSource (dict) --
Specifies the location of ML model data to deploy.
Note
Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) --
Specifies the S3 path of ML model data to deploy.
S3DataType (string) --
Specifies the type of ML model data to deploy.
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) --
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot (. )
A double dot (.. )
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) --
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelPackageName (string) --
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName (string) --
The inference specification name in the model package version.
MultiModelConfig (dict) --
Specifies additional configuration for multi-model endpoints.
ModelCacheSetting (string) --
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
Containers (list) --
The containers in the inference pipeline.
(dict) --
Describes the container, as part of model definition.
ContainerHostname (string) --
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline . If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
Image (string) --
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
ImageConfig (dict) --
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers .
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
RepositoryAccessMode (string) --
Set this to one of the following values:
Platform - The model image is hosted in Amazon ECR.
Vpc - The model image is hosted in a private Docker registry in your VPC.
RepositoryAuthConfig (dict) --
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
RepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Mode (string) --
Whether the container hosts a single model or multiple models.
ModelDataUrl (string) --
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters .
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
ModelDataSource (dict) --
Specifies the location of ML model data to deploy.
Note
Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) --
Specifies the S3 path of ML model data to deploy.
S3DataType (string) --
Specifies the type of ML model data to deploy.
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) --
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot (. )
A double dot (.. )
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) --
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelPackageName (string) --
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName (string) --
The inference specification name in the model package version.
MultiModelConfig (dict) --
Specifies additional configuration for multi-model endpoints.
ModelCacheSetting (string) --
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
InferenceExecutionConfig (dict) --
Specifies details of how containers in a multi-container endpoint are called.
Mode (string) --
How containers in a multi-container are run. The following values are valid.
SERIAL - Containers run as a serial pipeline.
DIRECT - Only the individual container that you specify is run.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this model has access to. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
CreationTime (datetime) --
A timestamp that shows when the model was created.
ModelArn (string) --
The Amazon Resource Name (ARN) of the model.
EnableNetworkIsolation (boolean) --
If True , no inbound or outbound network calls can be made to or from the model container.
DeploymentRecommendation (dict) --
A set of recommended deployment configurations for the model.
RecommendationStatus (string) --
Status of the deployment recommendation. The status NOT_APPLICABLE means that SageMaker is unable to provide a default recommendation for the model using the information provided. If the deployment status is IN_PROGRESS , retry your API call after a few seconds to get a COMPLETED deployment recommendation.
RealTimeInferenceRecommendations (list) --
A list of RealTimeInferenceRecommendation items.
(dict) --
The recommended configuration to use for Real-Time Inference.
RecommendationId (string) --
The recommendation ID which uniquely identifies each recommendation.
InstanceType (string) --
The recommended instance type for Real-Time Inference.
Environment (dict) --
The recommended environment variables to set in the model container for Real-Time Inference.
(string) --
(string) --
{'AdditionalInferenceSpecifications': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}},
'InferenceSpecification': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}}
Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.
To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
See also: AWS API Documentation
Request Syntax
client.describe_model_package(
ModelPackageName='string'
)
string
[REQUIRED]
The name or Amazon Resource Name (ARN) of the model package to describe.
When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
dict
Response Syntax
{
'ModelPackageName': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 123,
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'InferenceSpecification': {
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalS3DataSource': {
'S3DataType': 'S3Object',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
'SourceAlgorithmSpecification': {
'SourceAlgorithms': [
{
'ModelDataUrl': 'string',
'AlgorithmName': 'string'
},
]
},
'ValidationSpecification': {
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'ModelPackageStatusDetails': {
'ValidationStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
],
'ImageScanStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
]
},
'CertifyForMarketplace': True|False,
'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval',
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'ModelMetrics': {
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Bias': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PreTrainingReport': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PostTrainingReport': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'ApprovalDescription': 'string',
'Domain': 'string',
'Task': 'string',
'SamplePayloadUrl': 'string',
'CustomerMetadataProperties': {
'string': 'string'
},
'DriftCheckBaselines': {
'Bias': {
'ConfigFile': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PreTrainingConstraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PostTrainingConstraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'ConfigFile': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
'AdditionalInferenceSpecifications': [
{
'Name': 'string',
'Description': 'string',
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalS3DataSource': {
'S3DataType': 'S3Object',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
],
'SkipModelValidation': 'All'|'None'
}
Response Structure
(dict) --
ModelPackageName (string) --
The name of the model package being described.
ModelPackageGroupName (string) --
If the model is a versioned model, the name of the model group that the versioned model belongs to.
ModelPackageVersion (integer) --
The version of the model package.
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription (string) --
A brief summary of the model package.
CreationTime (datetime) --
A timestamp specifying when the model package was created.
InferenceSpecification (dict) --
Details about inference jobs that can be run with models based on this model package.
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) --
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) --
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) --
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
SourceAlgorithmSpecification (dict) --
Details about the algorithm that was used to create the model package.
SourceAlgorithms (list) --
A list of the algorithms that were used to create a model package.
(dict) --
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same Amazon Web Services region as the algorithm.
AlgorithmName (string) --
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ValidationSpecification (dict) --
Configurations for one or more transform jobs that SageMaker runs to test the model package.
ValidationRole (string) --
The IAM roles to be used for the validation of the model package.
ValidationProfiles (list) --
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.
(dict) --
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
ProfileName (string) --
The name of the profile for the model package.
TransformJobDefinition (dict) --
The TransformJobDefinition object that describes the transform job used for the validation of the model package.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
A description of the input source and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) --
Identifies the ML compute instances for the transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
ModelPackageStatus (string) --
The current status of the model package.
ModelPackageStatusDetails (dict) --
Details about the current status of the model package.
ValidationStatuses (list) --
The validation status of the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
ImageScanStatuses (list) --
The status of the scan of the Docker image container for the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
CertifyForMarketplace (boolean) --
Whether the model package is certified for listing on Amazon Web Services Marketplace.
ModelApprovalStatus (string) --
The approval status of the model package.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
ModelMetrics (dict) --
Metrics for the model.
ModelQuality (dict) --
Metrics that measure the quality of a model.
Statistics (dict) --
Model quality statistics.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
Model quality constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
ModelDataQuality (dict) --
Metrics that measure the quality of the input data for a model.
Statistics (dict) --
Data quality statistics for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
Data quality constraints for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Bias (dict) --
Metrics that measure bais in a model.
Report (dict) --
The bias report for a model
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
PreTrainingReport (dict) --
The pre-training bias report for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
PostTrainingReport (dict) --
The post-training bias report for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Explainability (dict) --
Metrics that help explain a model.
Report (dict) --
The explainability report for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
LastModifiedTime (datetime) --
The last time that the model package was modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
ApprovalDescription (string) --
A description provided for the model approval.
Domain (string) --
The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing.
Task (string) --
The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification.
SamplePayloadUrl (string) --
The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path points to a single gzip compressed tar archive (.tar.gz suffix).
CustomerMetadataProperties (dict) --
The metadata properties associated with the model package versions.
(string) --
(string) --
DriftCheckBaselines (dict) --
Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide .
Bias (dict) --
Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
ConfigFile (dict) --
The bias config file for a model.
ContentType (string) --
The type of content stored in the file source.
ContentDigest (string) --
The digest of the file source.
S3Uri (string) --
The Amazon S3 URI for the file source.
PreTrainingConstraints (dict) --
The pre-training constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
PostTrainingConstraints (dict) --
The post-training constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Explainability (dict) --
Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
Constraints (dict) --
The drift check explainability constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
ConfigFile (dict) --
The explainability config file for the model.
ContentType (string) --
The type of content stored in the file source.
ContentDigest (string) --
The digest of the file source.
S3Uri (string) --
The Amazon S3 URI for the file source.
ModelQuality (dict) --
Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
Statistics (dict) --
The drift check model quality statistics.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
The drift check model quality constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
ModelDataQuality (dict) --
Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.
Statistics (dict) --
The drift check model data quality statistics.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
The drift check model data quality constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
AdditionalInferenceSpecifications (list) --
An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
(dict) --
A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package
Name (string) --
A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
Description (string) --
A description of the additional Inference specification
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) --
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) --
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) --
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
SkipModelValidation (string) --
Indicates if you want to skip model validation.
{'PipelineStatus': {'Deleting'}}
Describes the details of a pipeline.
See also: AWS API Documentation
Request Syntax
client.describe_pipeline(
PipelineName='string'
)
string
[REQUIRED]
The name or Amazon Resource Name (ARN) of the pipeline to describe.
dict
Response Syntax
{
'PipelineArn': 'string',
'PipelineName': 'string',
'PipelineDisplayName': 'string',
'PipelineDefinition': 'string',
'PipelineDescription': 'string',
'RoleArn': 'string',
'PipelineStatus': 'Active'|'Deleting',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastRunTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'ParallelismConfiguration': {
'MaxParallelExecutionSteps': 123
}
}
Response Structure
(dict) --
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline.
PipelineName (string) --
The name of the pipeline.
PipelineDisplayName (string) --
The display name of the pipeline.
PipelineDefinition (string) --
The JSON pipeline definition.
PipelineDescription (string) --
The description of the pipeline.
RoleArn (string) --
The Amazon Resource Name (ARN) that the pipeline uses to execute.
PipelineStatus (string) --
The status of the pipeline execution.
CreationTime (datetime) --
The time when the pipeline was created.
LastModifiedTime (datetime) --
The time when the pipeline was last modified.
LastRunTime (datetime) --
The time when the pipeline was last run.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
ParallelismConfiguration (dict) --
Lists the parallelism configuration applied to the pipeline.
MaxParallelExecutionSteps (integer) --
The max number of steps that can be executed in parallel.
{'SpaceSettings': {'AppType': {'Canvas',
'DatasetManager',
'DetailedProfiler',
'Local',
'RSession',
'SageMakerLite',
'Savitur',
'VSCode'}}}
Describes the space.
See also: AWS API Documentation
Request Syntax
client.describe_space(
DomainId='string',
SpaceName='string'
)
string
[REQUIRED]
The ID of the associated Domain.
string
[REQUIRED]
The name of the space.
dict
Response Syntax
{
'DomainId': 'string',
'SpaceArn': 'string',
'SpaceName': 'string',
'HomeEfsFileSystemUid': 'string',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'SpaceSettings': {
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'CodeEditorAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
}
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
},
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
'SpaceStorageSettings': {
'EbsStorageSettings': {
'EbsVolumeSizeInGb': 123
}
},
'CustomFileSystems': [
{
'EFSFileSystem': {
'FileSystemId': 'string'
}
},
]
},
'OwnershipSettings': {
'OwnerUserProfileName': 'string'
},
'SpaceSharingSettings': {
'SharingType': 'Private'|'Shared'
},
'SpaceDisplayName': 'string',
'Url': 'string'
}
Response Structure
(dict) --
DomainId (string) --
The ID of the associated Domain.
SpaceArn (string) --
The space's Amazon Resource Name (ARN).
SpaceName (string) --
The name of the space.
HomeEfsFileSystemUid (string) --
The ID of the space's profile in the Amazon Elastic File System volume.
Status (string) --
The status.
LastModifiedTime (datetime) --
The last modified time.
CreationTime (datetime) --
The creation time.
FailureReason (string) --
The failure reason.
SpaceSettings (dict) --
A collection of space settings.
JupyterServerAppSettings (dict) --
The JupyterServer app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The KernelGateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image .
ImageName (string) --
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) --
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) --
The URL of the Git repository.
AppType (string) --
The type of app created within the space.
SpaceStorageSettings (dict) --
The storage settings for a private space.
EbsStorageSettings (dict) --
A collection of EBS storage settings for a private space.
EbsVolumeSizeInGb (integer) --
The size of an EBS storage volume for a private space.
CustomFileSystems (list) --
A file system, created by you, that you assign to a space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
(dict) --
A file system, created by you, that you assign to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystem (dict) --
A custom file system in Amazon EFS.
FileSystemId (string) --
The ID of your Amazon EFS file system.
OwnershipSettings (dict) --
The collection of ownership settings for a space.
OwnerUserProfileName (string) --
The user profile who is the owner of the private space.
SpaceSharingSettings (dict) --
The collection of space sharing settings for a space.
SharingType (string) --
Specifies the sharing type of the space.
SpaceDisplayName (string) --
The name of the space that appears in the Amazon SageMaker Studio UI.
Url (string) --
Returns the URL of the space. If the space is created with Amazon Web Services IAM Identity Center (Successor to Amazon Web Services Single Sign-On) authentication, users can navigate to the URL after appending the respective redirect parameter for the application type to be federated through Amazon Web Services IAM Identity Center.
The following application types are supported:
Studio Classic: &redirect=JupyterServer
JupyterLab: &redirect=JupyterLab
Code Editor, based on Code-OSS, Visual Studio Code - Open Source: &redirect=CodeEditor
{'StudioLifecycleConfigAppType': {'Savitur', 'VSCode'}}
Describes the Amazon SageMaker Studio Lifecycle Configuration.
See also: AWS API Documentation
Request Syntax
client.describe_studio_lifecycle_config(
StudioLifecycleConfigName='string'
)
string
[REQUIRED]
The name of the Amazon SageMaker Studio Lifecycle Configuration to describe.
dict
Response Syntax
{
'StudioLifecycleConfigArn': 'string',
'StudioLifecycleConfigName': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'StudioLifecycleConfigContent': 'string',
'StudioLifecycleConfigAppType': 'JupyterServer'|'KernelGateway'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'
}
Response Structure
(dict) --
StudioLifecycleConfigArn (string) --
The ARN of the Lifecycle Configuration to describe.
StudioLifecycleConfigName (string) --
The name of the Amazon SageMaker Studio Lifecycle Configuration that is described.
CreationTime (datetime) --
The creation time of the Amazon SageMaker Studio Lifecycle Configuration.
LastModifiedTime (datetime) --
This value is equivalent to CreationTime because Amazon SageMaker Studio Lifecycle Configurations are immutable.
StudioLifecycleConfigContent (string) --
The content of your Amazon SageMaker Studio Lifecycle Configuration script.
StudioLifecycleConfigAppType (string) --
The App type that the Lifecycle Configuration is attached to.
{'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'SecondaryStatus': {'Pending'},
'SecondaryStatusTransitions': {'Status': {'Pending'}}}
Returns information about a training job.
Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason , attributes like TrainingStartTime , TrainingTimeInSeconds , TrainingEndTime , and BillableTimeInSeconds may not be present in the response.
See also: AWS API Documentation
Request Syntax
client.describe_training_job(
TrainingJobName='string'
)
string
[REQUIRED]
The name of the training job.
dict
Response Syntax
{
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting'|'Pending',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False,
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'TrainingImageConfig': {
'TrainingRepositoryAccessMode': 'Platform'|'Vpc',
'TrainingRepositoryAuthConfig': {
'TrainingRepositoryCredentialsProviderArn': 'string'
}
}
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'KeepAlivePeriodInSeconds': 123,
'InstanceGroups': [
{
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge',
'InstanceCount': 123,
'InstanceGroupName': 'string'
},
]
},
'WarmPoolStatus': {
'Status': 'Available'|'Terminated'|'Reused'|'InUse',
'ResourceRetainedBillableTimeInSeconds': 123,
'ReusedByJob': 'string'
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting'|'Pending',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': 'string'
},
],
'FinalMetricDataList': [
{
'MetricName': 'string',
'Value': ...,
'Timestamp': datetime(2015, 1, 1)
},
],
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'TrainingTimeInSeconds': 123,
'BillableTimeInSeconds': 123,
'DebugHookConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
},
'DebugRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'TensorBoardOutputConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string'
},
'DebugRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'ProfilerConfig': {
'S3OutputPath': 'string',
'ProfilingIntervalInMilliseconds': 123,
'ProfilingParameters': {
'string': 'string'
},
'DisableProfiler': True|False
},
'ProfilerRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'ProfilerRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'ProfilingStatus': 'Enabled'|'Disabled',
'Environment': {
'string': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'InfraCheckConfig': {
'EnableInfraCheck': True|False
}
}
Response Structure
(dict) --
TrainingJobName (string) --
Name of the model training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling job that created the transform or training job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of an AutoML job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
TrainingJobStatus (string) --
The status of the training job.
SageMaker provides the following training job statuses:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus .
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see StatusMessage under SecondaryStatusTransition .
SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Interrupted - The job stopped because the managed spot training instances were interrupted.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
MaxWaitTimeExceeded - The job stopped because it exceeded the maximum allowed wait time.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
Warning
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTraining
DownloadingTrainingImage
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
(string) --
(string) --
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker .
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
For more information, see the note in the AlgorithmName parameter description.
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage , and vice versa.
If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
You use one of the SageMaker built-in algorithms
You use one of the following Prebuilt SageMaker Docker Images :
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
ContainerEntrypoint (list) --
The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
(string) --
TrainingImageConfig (dict) --
The configuration to use an image from a private Docker registry for a training job.
TrainingRepositoryAccessMode (string) --
The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to Vpc .
TrainingRepositoryAuthConfig (dict) --
An object containing authentication information for a private Docker registry containing your training images.
TrainingRepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
RoleArn (string) --
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig (list) --
An array of Channel objects that describes each data input channel.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
WarmPoolStatus (dict) --
The status of the warm pool associated with the training job.
Status (string) --
The status of the warm pool.
InUse : The warm pool is in use for the training job.
Available : The warm pool is available to reuse for a matching training job.
Reused : The warm pool moved to a matching training job for reuse.
Terminated : The warm pool is no longer available. Warm pools are unavailable if they are terminated by a user, terminated for a patch update, or terminated for exceeding the specified KeepAlivePeriodInSeconds .
ResourceRetainedBillableTimeInSeconds (integer) --
The billable time in seconds used by the warm pool. Billable time refers to the absolute wall-clock time.
Multiply ResourceRetainedBillableTimeInSeconds by the number of instances (InstanceCount ) in your training cluster to get the total compute time SageMaker bills you if you run warm pool training. The formula is as follows: ResourceRetainedBillableTimeInSeconds * InstanceCount .
ReusedByJob (string) --
The name of the matching training job that reused the warm pool.
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
CreationTime (datetime) --
A timestamp that indicates when the training job was created.
TrainingStartTime (datetime) --
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime . The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
TrainingEndTime (datetime) --
Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
LastModifiedTime (datetime) --
A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions (list) --
A history of all of the secondary statuses that the training job has transitioned through.
(dict) --
An array element of SecondaryStatusTransitions for DescribeTrainingJob . It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Status (string) --
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
StartTime (datetime) --
A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime (datetime) --
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage (string) --
A detailed description of the progress within a secondary status.
SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
Warning
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob , and StatusMessage together. For example, at the start of a training job, you might see the following:
TrainingJobStatus - InProgress
SecondaryStatus - Training
StatusMessage - Downloading the training image
FinalMetricDataList (list) --
A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True . If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds. Billable time refers to the absolute wall-clock time.
Multiply BillableTimeInSeconds by the number of instances (InstanceCount ) in your training cluster to get the total compute time SageMaker bills you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .
You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100 . For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.
DebugHookConfig (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
LocalPath (string) --
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for metrics and tensors.
HookParameters (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters.
(string) --
(string) --
CollectionConfigurations (list) --
Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(dict) --
Configuration information for the Amazon SageMaker Debugger output tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
(string) --
(string) --
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
DebugRuleConfigurations (list) --
Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
(dict) --
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a custom rule for debugging a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
TensorBoardOutputConfig (dict) --
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Evaluation status of Amazon SageMaker Debugger rules for debugging on a training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration.
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
ProfilerConfig (dict) --
Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath (string) --
Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds (integer) --
A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters (dict) --
Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig , PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(string) --
(string) --
DisableProfiler (boolean) --
Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to True .
ProfilerRuleConfigurations (list) --
Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
(dict) --
Configuration information for profiling rules.
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container Registry Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a custom rule for profiling a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
ProfilerRuleEvaluationStatuses (list) --
Evaluation status of Amazon SageMaker Debugger rules for profiling on a training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration.
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
ProfilingStatus (string) --
Profiling status of a training job.
Environment (dict) --
The environment variables to set in the Docker container.
(string) --
(string) --
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
InfraCheckConfig (dict) --
Contains information about the infrastructure health check configuration for the training job.
EnableInfraCheck (boolean) --
Enables an infrastructure health check.
{'Resource': {'ImageVersion', 'Image'}}
An auto-complete API for the search functionality in the SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters , Tags , and Metrics .
See also: AWS API Documentation
Request Syntax
client.get_search_suggestions(
Resource='TrainingJob'|'Experiment'|'ExperimentTrial'|'ExperimentTrialComponent'|'Endpoint'|'Model'|'ModelPackage'|'ModelPackageGroup'|'Pipeline'|'PipelineExecution'|'FeatureGroup'|'FeatureMetadata'|'Image'|'ImageVersion'|'Project'|'HyperParameterTuningJob'|'ModelCard',
SuggestionQuery={
'PropertyNameQuery': {
'PropertyNameHint': 'string'
}
}
)
string
[REQUIRED]
The name of the SageMaker resource to search for.
dict
Limits the property names that are included in the response.
PropertyNameQuery (dict) --
Defines a property name hint. Only property names that begin with the specified hint are included in the response.
PropertyNameHint (string) -- [REQUIRED]
Text that begins a property's name.
dict
Response Syntax
{
'PropertyNameSuggestions': [
{
'PropertyName': 'string'
},
]
}
Response Structure
(dict) --
PropertyNameSuggestions (list) --
A list of property names for a Resource that match a SuggestionQuery .
(dict) --
A property name returned from a GetSearchSuggestions call that specifies a value in the PropertyNameQuery field.
PropertyName (string) --
A suggested property name based on what you entered in the search textbox in the SageMaker console.
{'AppImageConfigs': {'JupyterLabAppImageConfig': {'FileSystemConfig': {'DefaultGid': 'integer',
'DefaultUid': 'integer',
'MountPath': 'string'}}}}
Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.
See also: AWS API Documentation
Request Syntax
client.list_app_image_configs(
MaxResults=123,
NextToken='string',
NameContains='string',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
ModifiedTimeBefore=datetime(2015, 1, 1),
ModifiedTimeAfter=datetime(2015, 1, 1),
SortBy='CreationTime'|'LastModifiedTime'|'Name',
SortOrder='Ascending'|'Descending'
)
integer
The total number of items to return in the response. If the total number of items available is more than the value specified, a NextToken is provided in the response. To resume pagination, provide the NextToken value in the as part of a subsequent call. The default value is 10.
string
If the previous call to ListImages didn't return the full set of AppImageConfigs, the call returns a token for getting the next set of AppImageConfigs.
string
A filter that returns only AppImageConfigs whose name contains the specified string.
datetime
A filter that returns only AppImageConfigs created on or before the specified time.
datetime
A filter that returns only AppImageConfigs created on or after the specified time.
datetime
A filter that returns only AppImageConfigs modified on or before the specified time.
datetime
A filter that returns only AppImageConfigs modified on or after the specified time.
string
The property used to sort results. The default value is CreationTime .
string
The sort order. The default value is Descending .
dict
Response Syntax
{
'NextToken': 'string',
'AppImageConfigs': [
{
'AppImageConfigArn': 'string',
'AppImageConfigName': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'KernelGatewayImageConfig': {
'KernelSpecs': [
{
'Name': 'string',
'DisplayName': 'string'
},
],
'FileSystemConfig': {
'MountPath': 'string',
'DefaultUid': 123,
'DefaultGid': 123
}
},
'JupyterLabAppImageConfig': {
'FileSystemConfig': {
'MountPath': 'string',
'DefaultUid': 123,
'DefaultGid': 123
},
'ContainerConfig': {
'ContainerArguments': [
'string',
],
'ContainerEntrypoint': [
'string',
],
'ContainerEnvironmentVariables': {
'string': 'string'
}
}
}
},
]
}
Response Structure
(dict) --
NextToken (string) --
A token for getting the next set of AppImageConfigs, if there are any.
AppImageConfigs (list) --
A list of AppImageConfigs and their properties.
(dict) --
The configuration for running a SageMaker image as a KernelGateway app.
AppImageConfigArn (string) --
The Amazon Resource Name (ARN) of the AppImageConfig.
AppImageConfigName (string) --
The name of the AppImageConfig. Must be unique to your account.
CreationTime (datetime) --
When the AppImageConfig was created.
LastModifiedTime (datetime) --
When the AppImageConfig was last modified.
KernelGatewayImageConfig (dict) --
The configuration for the file system and kernels in the SageMaker image.
KernelSpecs (list) --
The specification of the Jupyter kernels in the image.
(dict) --
The specification of a Jupyter kernel.
Name (string) --
The name of the Jupyter kernel in the image. This value is case sensitive.
DisplayName (string) --
The display name of the kernel.
FileSystemConfig (dict) --
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
MountPath (string) --
The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
DefaultUid (integer) --
The default POSIX user ID (UID). If not specified, defaults to 1000 .
DefaultGid (integer) --
The default POSIX group ID (GID). If not specified, defaults to 100 .
JupyterLabAppImageConfig (dict) --
The configuration for the file system and the runtime, such as the environment variables and entry point.
FileSystemConfig (dict) --
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
MountPath (string) --
The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
DefaultUid (integer) --
The default POSIX user ID (UID). If not specified, defaults to 1000 .
DefaultGid (integer) --
The default POSIX group ID (GID). If not specified, defaults to 100 .
ContainerConfig (dict) --
The configuration used to run the application image container.
ContainerArguments (list) --
The arguments for the container when you're running the application.
(string) --
ContainerEntrypoint (list) --
The entrypoint used to run the application in the container.
(string) --
ContainerEnvironmentVariables (dict) --
The environment variables to set in the container
(string) --
(string) --
{'Apps': {'AppType': {'Canvas',
'DatasetManager',
'DetailedProfiler',
'Local',
'RSession',
'SageMakerLite',
'Savitur',
'VSCode'}}}
Lists apps.
See also: AWS API Documentation
Request Syntax
client.list_apps(
NextToken='string',
MaxResults=123,
SortOrder='Ascending'|'Descending',
SortBy='CreationTime',
DomainIdEquals='string',
UserProfileNameEquals='string',
SpaceNameEquals='string'
)
string
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
integer
The total number of items to return in the response. If the total number of items available is more than the value specified, a NextToken is provided in the response. To resume pagination, provide the NextToken value in the as part of a subsequent call. The default value is 10.
string
The sort order for the results. The default is Ascending.
string
The parameter by which to sort the results. The default is CreationTime.
string
A parameter to search for the domain ID.
string
A parameter to search by user profile name. If SpaceNameEquals is set, then this value cannot be set.
string
A parameter to search by space name. If UserProfileNameEquals is set, then this value cannot be set.
dict
Response Syntax
{
'Apps': [
{
'DomainId': 'string',
'UserProfileName': 'string',
'SpaceName': 'string',
'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
'AppName': 'string',
'Status': 'Deleted'|'Deleting'|'Failed'|'InService'|'Pending',
'CreationTime': datetime(2015, 1, 1),
'ResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Apps (list) --
The list of apps.
(dict) --
Details about an Amazon SageMaker app.
DomainId (string) --
The domain ID.
UserProfileName (string) --
The user profile name.
SpaceName (string) --
The name of the space.
AppType (string) --
The type of app.
AppName (string) --
The name of the app.
Status (string) --
The status.
CreationTime (datetime) --
The creation time.
ResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
NextToken (string) --
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
{'AssociationType': {'SameAs'}}
Response {'AssociationSummaries': {'AssociationType': {'SameAs'}}}
Lists the associations in your account and their properties.
See also: AWS API Documentation
Request Syntax
client.list_associations(
SourceArn='string',
DestinationArn='string',
SourceType='string',
DestinationType='string',
AssociationType='ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced'|'SameAs',
CreatedAfter=datetime(2015, 1, 1),
CreatedBefore=datetime(2015, 1, 1),
SortBy='SourceArn'|'DestinationArn'|'SourceType'|'DestinationType'|'CreationTime',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123
)
string
A filter that returns only associations with the specified source ARN.
string
A filter that returns only associations with the specified destination Amazon Resource Name (ARN).
string
A filter that returns only associations with the specified source type.
string
A filter that returns only associations with the specified destination type.
string
A filter that returns only associations of the specified type.
datetime
A filter that returns only associations created on or after the specified time.
datetime
A filter that returns only associations created on or before the specified time.
string
The property used to sort results. The default value is CreationTime .
string
The sort order. The default value is Descending .
string
If the previous call to ListAssociations didn't return the full set of associations, the call returns a token for getting the next set of associations.
integer
The maximum number of associations to return in the response. The default value is 10.
dict
Response Syntax
{
'AssociationSummaries': [
{
'SourceArn': 'string',
'DestinationArn': 'string',
'SourceType': 'string',
'DestinationType': 'string',
'AssociationType': 'ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced'|'SameAs',
'SourceName': 'string',
'DestinationName': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
AssociationSummaries (list) --
A list of associations and their properties.
(dict) --
Lists a summary of the properties of an association. An association is an entity that links other lineage or experiment entities. An example would be an association between a training job and a model.
SourceArn (string) --
The ARN of the source.
DestinationArn (string) --
The Amazon Resource Name (ARN) of the destination.
SourceType (string) --
The source type.
DestinationType (string) --
The destination type.
AssociationType (string) --
The type of the association.
SourceName (string) --
The name of the source.
DestinationName (string) --
The name of the destination.
CreationTime (datetime) --
When the association was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
NextToken (string) --
A token for getting the next set of associations, if there are any.
{'CompilationJobSummaries': {'CompilationTargetDevice': {'ml_c6g',
'ml_m6g',
'rasp4b'}}}
Lists model compilation jobs that satisfy various filters.
To create a model compilation job, use CreateCompilationJob . To get information about a particular model compilation job you have created, use DescribeCompilationJob .
See also: AWS API Documentation
Request Syntax
client.list_compilation_jobs(
NextToken='string',
MaxResults=123,
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending'
)
string
If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken . To retrieve the next set of model compilation jobs, use the token in the next request.
integer
The maximum number of model compilation jobs to return in the response.
datetime
A filter that returns the model compilation jobs that were created after a specified time.
datetime
A filter that returns the model compilation jobs that were created before a specified time.
datetime
A filter that returns the model compilation jobs that were modified after a specified time.
datetime
A filter that returns the model compilation jobs that were modified before a specified time.
string
A filter that returns the model compilation jobs whose name contains a specified string.
string
A filter that retrieves model compilation jobs with a specific CompilationJobStatus status.
string
The field by which to sort results. The default is CreationTime .
string
The sort order for results. The default is Ascending .
dict
Response Syntax
{
'CompilationJobSummaries': [
{
'CompilationJobName': 'string',
'CompilationJobArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'CompilationStartTime': datetime(2015, 1, 1),
'CompilationEndTime': datetime(2015, 1, 1),
'CompilationTargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_m6g'|'ml_c4'|'ml_c5'|'ml_c6g'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'ml_inf2'|'ml_trn1'|'ml_eia2'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'rasp4b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv2'|'amba_cv22'|'amba_cv25'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm'|'imx8mplus',
'CompilationTargetPlatformOs': 'ANDROID'|'LINUX',
'CompilationTargetPlatformArch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF',
'CompilationTargetPlatformAccelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'|'NNA',
'LastModifiedTime': datetime(2015, 1, 1),
'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
CompilationJobSummaries (list) --
An array of CompilationJobSummary objects, each describing a model compilation job.
(dict) --
A summary of a model compilation job.
CompilationJobName (string) --
The name of the model compilation job that you want a summary for.
CompilationJobArn (string) --
The Amazon Resource Name (ARN) of the model compilation job.
CreationTime (datetime) --
The time when the model compilation job was created.
CompilationStartTime (datetime) --
The time when the model compilation job started.
CompilationEndTime (datetime) --
The time when the model compilation job completed.
CompilationTargetDevice (string) --
The type of device that the model will run on after the compilation job has completed.
CompilationTargetPlatformOs (string) --
The type of OS that the model will run on after the compilation job has completed.
CompilationTargetPlatformArch (string) --
The type of architecture that the model will run on after the compilation job has completed.
CompilationTargetPlatformAccelerator (string) --
The type of accelerator that the model will run on after the compilation job has completed.
LastModifiedTime (datetime) --
The time when the model compilation job was last modified.
CompilationJobStatus (string) --
The status of the model compilation job.
NextToken (string) --
If the response is truncated, Amazon SageMaker returns this NextToken . To retrieve the next set of model compilation jobs, use this token in the next request.
{'StatusEquals': {'DeleteFailed', 'Deleting'}}
Response {'HyperParameterTuningJobSummaries': {'HyperParameterTuningJobStatus': {'DeleteFailed',
'Deleting'}}}
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
See also: AWS API Documentation
Request Syntax
client.list_hyper_parameter_tuning_jobs(
NextToken='string',
MaxResults=123,
SortBy='Name'|'Status'|'CreationTime',
SortOrder='Ascending'|'Descending',
NameContains='string',
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping'|'Deleting'|'DeleteFailed'
)
string
If the result of the previous ListHyperParameterTuningJobs request was truncated, the response includes a NextToken . To retrieve the next set of tuning jobs, use the token in the next request.
integer
The maximum number of tuning jobs to return. The default value is 10.
string
The field to sort results by. The default is Name .
string
The sort order for results. The default is Ascending .
string
A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.
datetime
A filter that returns only tuning jobs that were created after the specified time.
datetime
A filter that returns only tuning jobs that were created before the specified time.
datetime
A filter that returns only tuning jobs that were modified after the specified time.
datetime
A filter that returns only tuning jobs that were modified before the specified time.
string
A filter that returns only tuning jobs with the specified status.
dict
Response Syntax
{
'HyperParameterTuningJobSummaries': [
{
'HyperParameterTuningJobName': 'string',
'HyperParameterTuningJobArn': 'string',
'HyperParameterTuningJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping'|'Deleting'|'DeleteFailed',
'Strategy': 'Bayesian'|'Random'|'Hyperband'|'Grid',
'CreationTime': datetime(2015, 1, 1),
'HyperParameterTuningEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'TrainingJobStatusCounters': {
'Completed': 123,
'InProgress': 123,
'RetryableError': 123,
'NonRetryableError': 123,
'Stopped': 123
},
'ObjectiveStatusCounters': {
'Succeeded': 123,
'Pending': 123,
'Failed': 123
},
'ResourceLimits': {
'MaxNumberOfTrainingJobs': 123,
'MaxParallelTrainingJobs': 123,
'MaxRuntimeInSeconds': 123
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
HyperParameterTuningJobSummaries (list) --
A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the ListHyperParameterTuningJobs request returned.
(dict) --
Provides summary information about a hyperparameter tuning job.
HyperParameterTuningJobName (string) --
The name of the tuning job.
HyperParameterTuningJobArn (string) --
The Amazon Resource Name (ARN) of the tuning job.
HyperParameterTuningJobStatus (string) --
The status of the tuning job.
Strategy (string) --
Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to evaluate at each iteration.
CreationTime (datetime) --
The date and time that the tuning job was created.
HyperParameterTuningEndTime (datetime) --
The date and time that the tuning job ended.
LastModifiedTime (datetime) --
The date and time that the tuning job was modified.
TrainingJobStatusCounters (dict) --
The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.
Completed (integer) --
The number of completed training jobs launched by the hyperparameter tuning job.
InProgress (integer) --
The number of in-progress training jobs launched by a hyperparameter tuning job.
RetryableError (integer) --
The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
NonRetryableError (integer) --
The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
Stopped (integer) --
The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
ObjectiveStatusCounters (dict) --
The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.
Succeeded (integer) --
The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending (integer) --
The number of training jobs that are in progress and pending evaluation of their final objective metric.
Failed (integer) --
The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
ResourceLimits (dict) --
The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.
MaxNumberOfTrainingJobs (integer) --
The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs (integer) --
The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
MaxRuntimeInSeconds (integer) --
The maximum time in seconds that a hyperparameter tuning job can run.
NextToken (string) --
If the result of this ListHyperParameterTuningJobs request was truncated, the response includes a NextToken . To retrieve the next set of tuning jobs, use the token in the next request.
{'Status': {'DELETING', 'DELETED'}}
Response {'Steps': {'InferenceBenchmark': {'EndpointConfiguration': {'InstanceType': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}},
'Status': {'DELETING', 'DELETED'}}}
Returns a list of the subtasks for an Inference Recommender job.
The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.
See also: AWS API Documentation
Request Syntax
client.list_inference_recommendations_job_steps(
JobName='string',
Status='PENDING'|'IN_PROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'DELETED',
StepType='BENCHMARK',
MaxResults=123,
NextToken='string'
)
string
[REQUIRED]
The name for the Inference Recommender job.
string
A filter to return benchmarks of a specified status. If this field is left empty, then all benchmarks are returned.
string
A filter to return details about the specified type of subtask.
BENCHMARK : Evaluate the performance of your model on different instance types.
integer
The maximum number of results to return.
string
A token that you can specify to return more results from the list. Specify this field if you have a token that was returned from a previous request.
dict
Response Syntax
{
'Steps': [
{
'StepType': 'BENCHMARK',
'JobName': 'string',
'Status': 'PENDING'|'IN_PROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'DELETED',
'InferenceBenchmark': {
'Metrics': {
'CostPerHour': ...,
'CostPerInference': ...,
'MaxInvocations': 123,
'ModelLatency': 123,
'CpuUtilization': ...,
'MemoryUtilization': ...,
'ModelSetupTime': 123
},
'EndpointMetrics': {
'MaxInvocations': 123,
'ModelLatency': 123
},
'EndpointConfiguration': {
'EndpointName': 'string',
'VariantName': 'string',
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
'InitialInstanceCount': 123,
'ServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
}
},
'ModelConfiguration': {
'InferenceSpecificationName': 'string',
'EnvironmentParameters': [
{
'Key': 'string',
'ValueType': 'string',
'Value': 'string'
},
],
'CompilationJobName': 'string'
},
'FailureReason': 'string',
'InvocationEndTime': datetime(2015, 1, 1),
'InvocationStartTime': datetime(2015, 1, 1)
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Steps (list) --
A list of all subtask details in Inference Recommender.
(dict) --
A returned array object for the Steps response field in the ListInferenceRecommendationsJobSteps API command.
StepType (string) --
The type of the subtask.
BENCHMARK : Evaluate the performance of your model on different instance types.
JobName (string) --
The name of the Inference Recommender job.
Status (string) --
The current status of the benchmark.
InferenceBenchmark (dict) --
The details for a specific benchmark.
Metrics (dict) --
The metrics of recommendations.
CostPerHour (float) --
Defines the cost per hour for the instance.
CostPerInference (float) --
Defines the cost per inference for the instance .
MaxInvocations (integer) --
The expected maximum number of requests per minute for the instance.
ModelLatency (integer) --
The expected model latency at maximum invocation per minute for the instance.
CpuUtilization (float) --
The expected CPU utilization at maximum invocations per minute for the instance.
NaN indicates that the value is not available.
MemoryUtilization (float) --
The expected memory utilization at maximum invocations per minute for the instance.
NaN indicates that the value is not available.
ModelSetupTime (integer) --
The time it takes to launch new compute resources for a serverless endpoint. The time can vary depending on the model size, how long it takes to download the model, and the start-up time of the container.
NaN indicates that the value is not available.
EndpointMetrics (dict) --
The metrics for an existing endpoint compared in an Inference Recommender job.
MaxInvocations (integer) --
The expected maximum number of requests per minute for the instance.
ModelLatency (integer) --
The expected model latency at maximum invocations per minute for the instance.
EndpointConfiguration (dict) --
The endpoint configuration made by Inference Recommender during a recommendation job.
EndpointName (string) --
The name of the endpoint made during a recommendation job.
VariantName (string) --
The name of the production variant (deployed model) made during a recommendation job.
InstanceType (string) --
The instance type recommended by Amazon SageMaker Inference Recommender.
InitialInstanceCount (integer) --
The number of instances recommended to launch initially.
ServerlessConfig (dict) --
Specifies the serverless configuration for an endpoint variant.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
ModelConfiguration (dict) --
Defines the model configuration. Includes the specification name and environment parameters.
InferenceSpecificationName (string) --
The inference specification name in the model package version.
EnvironmentParameters (list) --
Defines the environment parameters that includes key, value types, and values.
(dict) --
A list of environment parameters suggested by the Amazon SageMaker Inference Recommender.
Key (string) --
The environment key suggested by the Amazon SageMaker Inference Recommender.
ValueType (string) --
The value type suggested by the Amazon SageMaker Inference Recommender.
Value (string) --
The value suggested by the Amazon SageMaker Inference Recommender.
CompilationJobName (string) --
The name of the compilation job used to create the recommended model artifacts.
FailureReason (string) --
The reason why a benchmark failed.
InvocationEndTime (datetime) --
A timestamp that shows when the benchmark completed.
InvocationStartTime (datetime) --
A timestamp that shows when the benchmark started.
NextToken (string) --
A token that you can specify in your next request to return more results from the list.
{'StatusEquals': {'DELETING', 'DELETED'}}
Response {'InferenceRecommendationsJobs': {'Status': {'DELETING', 'DELETED'}}}
Lists recommendation jobs that satisfy various filters.
See also: AWS API Documentation
Request Syntax
client.list_inference_recommendations_jobs(
CreationTimeAfter=datetime(2015, 1, 1),
CreationTimeBefore=datetime(2015, 1, 1),
LastModifiedTimeAfter=datetime(2015, 1, 1),
LastModifiedTimeBefore=datetime(2015, 1, 1),
NameContains='string',
StatusEquals='PENDING'|'IN_PROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'DELETED',
SortBy='Name'|'CreationTime'|'Status',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123,
ModelNameEquals='string',
ModelPackageVersionArnEquals='string'
)
datetime
A filter that returns only jobs created after the specified time (timestamp).
datetime
A filter that returns only jobs created before the specified time (timestamp).
datetime
A filter that returns only jobs that were last modified after the specified time (timestamp).
datetime
A filter that returns only jobs that were last modified before the specified time (timestamp).
string
A string in the job name. This filter returns only recommendations whose name contains the specified string.
string
A filter that retrieves only inference recommendations jobs with a specific status.
string
The parameter by which to sort the results.
string
The sort order for the results.
string
If the response to a previous ListInferenceRecommendationsJobsRequest request was truncated, the response includes a NextToken . To retrieve the next set of recommendations, use the token in the next request.
integer
The maximum number of recommendations to return in the response.
string
A filter that returns only jobs that were created for this model.
string
A filter that returns only jobs that were created for this versioned model package.
dict
Response Syntax
{
'InferenceRecommendationsJobs': [
{
'JobName': 'string',
'JobDescription': 'string',
'JobType': 'Default'|'Advanced',
'JobArn': 'string',
'Status': 'PENDING'|'IN_PROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'DELETED',
'CreationTime': datetime(2015, 1, 1),
'CompletionTime': datetime(2015, 1, 1),
'RoleArn': 'string',
'LastModifiedTime': datetime(2015, 1, 1),
'FailureReason': 'string',
'ModelName': 'string',
'SamplePayloadUrl': 'string',
'ModelPackageVersionArn': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
InferenceRecommendationsJobs (list) --
The recommendations created from the Amazon SageMaker Inference Recommender job.
(dict) --
A structure that contains a list of recommendation jobs.
JobName (string) --
The name of the job.
JobDescription (string) --
The job description.
JobType (string) --
The recommendation job type.
JobArn (string) --
The Amazon Resource Name (ARN) of the recommendation job.
Status (string) --
The status of the job.
CreationTime (datetime) --
A timestamp that shows when the job was created.
CompletionTime (datetime) --
A timestamp that shows when the job completed.
RoleArn (string) --
The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
LastModifiedTime (datetime) --
A timestamp that shows when the job was last modified.
FailureReason (string) --
If the job fails, provides information why the job failed.
ModelName (string) --
The name of the created model.
SamplePayloadUrl (string) --
The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
ModelPackageVersionArn (string) --
The Amazon Resource Name (ARN) of a versioned model package.
NextToken (string) --
A token for getting the next set of recommendations, if there are any.
{'Spaces': {'SpaceSettingsSummary': {'AppType': {'Canvas',
'DatasetManager',
'DetailedProfiler',
'Local',
'RSession',
'SageMakerLite',
'Savitur',
'VSCode'}}}}
Lists spaces.
See also: AWS API Documentation
Request Syntax
client.list_spaces(
NextToken='string',
MaxResults=123,
SortOrder='Ascending'|'Descending',
SortBy='CreationTime'|'LastModifiedTime',
DomainIdEquals='string',
SpaceNameContains='string'
)
string
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
integer
The total number of items to return in the response. If the total number of items available is more than the value specified, a NextToken is provided in the response. To resume pagination, provide the NextToken value in the as part of a subsequent call. The default value is 10.
string
The sort order for the results. The default is Ascending .
string
The parameter by which to sort the results. The default is CreationTime .
string
A parameter to search for the Domain ID.
string
A parameter by which to filter the results.
dict
Response Syntax
{
'Spaces': [
{
'DomainId': 'string',
'SpaceName': 'string',
'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SpaceSettingsSummary': {
'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
'SpaceStorageSettings': {
'EbsStorageSettings': {
'EbsVolumeSizeInGb': 123
}
}
},
'SpaceSharingSettingsSummary': {
'SharingType': 'Private'|'Shared'
},
'OwnershipSettingsSummary': {
'OwnerUserProfileName': 'string'
},
'SpaceDisplayName': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Spaces (list) --
The list of spaces.
(dict) --
The space's details.
DomainId (string) --
The ID of the associated Domain.
SpaceName (string) --
The name of the space.
Status (string) --
The status.
CreationTime (datetime) --
The creation time.
LastModifiedTime (datetime) --
The last modified time.
SpaceSettingsSummary (dict) --
Specifies summary information about the space settings.
AppType (string) --
The type of app created within the space.
SpaceStorageSettings (dict) --
The storage settings for a private space.
EbsStorageSettings (dict) --
A collection of EBS storage settings for a private space.
EbsVolumeSizeInGb (integer) --
The size of an EBS storage volume for a private space.
SpaceSharingSettingsSummary (dict) --
Specifies summary information about the space sharing settings.
SharingType (string) --
Specifies the sharing type of the space.
OwnershipSettingsSummary (dict) --
Specifies summary information about the ownership settings.
OwnerUserProfileName (string) --
The user profile who is the owner of the private space.
SpaceDisplayName (string) --
The name of the space that appears in the Studio UI.
NextToken (string) --
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
{'AppTypeEquals': {'Savitur', 'VSCode'}}
Response {'StudioLifecycleConfigs': {'StudioLifecycleConfigAppType': {'Savitur',
'VSCode'}}}
Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account.
See also: AWS API Documentation
Request Syntax
client.list_studio_lifecycle_configs(
MaxResults=123,
NextToken='string',
NameContains='string',
AppTypeEquals='JupyterServer'|'KernelGateway'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab',
CreationTimeBefore=datetime(2015, 1, 1),
CreationTimeAfter=datetime(2015, 1, 1),
ModifiedTimeBefore=datetime(2015, 1, 1),
ModifiedTimeAfter=datetime(2015, 1, 1),
SortBy='CreationTime'|'LastModifiedTime'|'Name',
SortOrder='Ascending'|'Descending'
)
integer
The total number of items to return in the response. If the total number of items available is more than the value specified, a NextToken is provided in the response. To resume pagination, provide the NextToken value in the as part of a subsequent call. The default value is 10.
string
If the previous call to ListStudioLifecycleConfigs didn't return the full set of Lifecycle Configurations, the call returns a token for getting the next set of Lifecycle Configurations.
string
A string in the Lifecycle Configuration name. This filter returns only Lifecycle Configurations whose name contains the specified string.
string
A parameter to search for the App Type to which the Lifecycle Configuration is attached.
datetime
A filter that returns only Lifecycle Configurations created on or before the specified time.
datetime
A filter that returns only Lifecycle Configurations created on or after the specified time.
datetime
A filter that returns only Lifecycle Configurations modified before the specified time.
datetime
A filter that returns only Lifecycle Configurations modified after the specified time.
string
The property used to sort results. The default value is CreationTime.
string
The sort order. The default value is Descending.
dict
Response Syntax
{
'NextToken': 'string',
'StudioLifecycleConfigs': [
{
'StudioLifecycleConfigArn': 'string',
'StudioLifecycleConfigName': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'StudioLifecycleConfigAppType': 'JupyterServer'|'KernelGateway'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'
},
]
}
Response Structure
(dict) --
NextToken (string) --
If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
StudioLifecycleConfigs (list) --
A list of Lifecycle Configurations and their properties.
(dict) --
Details of the Amazon SageMaker Studio Lifecycle Configuration.
StudioLifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration.
StudioLifecycleConfigName (string) --
The name of the Amazon SageMaker Studio Lifecycle Configuration.
CreationTime (datetime) --
The creation time of the Amazon SageMaker Studio Lifecycle Configuration.
LastModifiedTime (datetime) --
This value is equivalent to CreationTime because Amazon SageMaker Studio Lifecycle Configurations are immutable.
StudioLifecycleConfigAppType (string) --
The App type to which the Lifecycle Configuration is attached.
{'Edges': {'AssociationType': {'SameAs'}}}
Use this action to inspect your lineage and discover relationships between entities. For more information, see Querying Lineage Entities in the Amazon SageMaker Developer Guide .
See also: AWS API Documentation
Request Syntax
client.query_lineage(
StartArns=[
'string',
],
Direction='Both'|'Ascendants'|'Descendants',
IncludeEdges=True|False,
Filters={
'Types': [
'string',
],
'LineageTypes': [
'TrialComponent'|'Artifact'|'Context'|'Action',
],
'CreatedBefore': datetime(2015, 1, 1),
'CreatedAfter': datetime(2015, 1, 1),
'ModifiedBefore': datetime(2015, 1, 1),
'ModifiedAfter': datetime(2015, 1, 1),
'Properties': {
'string': 'string'
}
},
MaxDepth=123,
MaxResults=123,
NextToken='string'
)
list
A list of resource Amazon Resource Name (ARN) that represent the starting point for your lineage query.
(string) --
string
Associations between lineage entities have a direction. This parameter determines the direction from the StartArn(s) that the query traverses.
boolean
Setting this value to True retrieves not only the entities of interest but also the Associations and lineage entities on the path. Set to False to only return lineage entities that match your query.
dict
A set of filtering parameters that allow you to specify which entities should be returned.
Properties - Key-value pairs to match on the lineage entities' properties.
LineageTypes - A set of lineage entity types to match on. For example: TrialComponent , Artifact , or Context .
CreatedBefore - Filter entities created before this date.
ModifiedBefore - Filter entities modified before this date.
ModifiedAfter - Filter entities modified after this date.
Types (list) --
Filter the lineage entities connected to the StartArn by type. For example: DataSet , Model , Endpoint , or ModelDeployment .
(string) --
LineageTypes (list) --
Filter the lineage entities connected to the StartArn (s) by the type of the lineage entity.
(string) --
CreatedBefore (datetime) --
Filter the lineage entities connected to the StartArn (s) by created date.
CreatedAfter (datetime) --
Filter the lineage entities connected to the StartArn (s) after the create date.
ModifiedBefore (datetime) --
Filter the lineage entities connected to the StartArn (s) before the last modified date.
ModifiedAfter (datetime) --
Filter the lineage entities connected to the StartArn (s) after the last modified date.
Properties (dict) --
Filter the lineage entities connected to the StartArn (s) by a set if property key value pairs. If multiple pairs are provided, an entity is included in the results if it matches any of the provided pairs.
(string) --
(string) --
integer
The maximum depth in lineage relationships from the StartArns that are traversed. Depth is a measure of the number of Associations from the StartArn entity to the matched results.
integer
Limits the number of vertices in the results. Use the NextToken in a response to to retrieve the next page of results.
string
Limits the number of vertices in the request. Use the NextToken in a response to to retrieve the next page of results.
dict
Response Syntax
{
'Vertices': [
{
'Arn': 'string',
'Type': 'string',
'LineageType': 'TrialComponent'|'Artifact'|'Context'|'Action'
},
],
'Edges': [
{
'SourceArn': 'string',
'DestinationArn': 'string',
'AssociationType': 'ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced'|'SameAs'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Vertices (list) --
A list of vertices connected to the start entity(ies) in the lineage graph.
(dict) --
A lineage entity connected to the starting entity(ies).
Arn (string) --
The Amazon Resource Name (ARN) of the lineage entity resource.
Type (string) --
The type of the lineage entity resource. For example: DataSet , Model , Endpoint , etc...
LineageType (string) --
The type of resource of the lineage entity.
Edges (list) --
A list of edges that connect vertices in the response.
(dict) --
A directed edge connecting two lineage entities.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source lineage entity of the directed edge.
DestinationArn (string) --
The Amazon Resource Name (ARN) of the destination lineage entity of the directed edge.
AssociationType (string) --
The type of the Association(Edge) between the source and destination. For example ContributedTo , Produced , or DerivedFrom .
NextToken (string) --
Limits the number of vertices in the response. Use the NextToken in a response to to retrieve the next page of results.
{'Resource': {'ImageVersion', 'Image'}}
Response {'Results': {'FeatureGroup': {'OfflineStoreConfig': {'TableFormat': {'Default'}}},
'HyperParameterTuningJob': {'HyperParameterTuningJobStatus': {'DeleteFailed',
'Deleting'},
'TrainingJobDefinition': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}}},
'TrainingJobDefinitions': {'HyperParameterTuningResourceConfig': {'InstanceConfigs': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}}}},
'Model': {'Model': {'DeploymentRecommendation': {'RealTimeInferenceRecommendations': {'InstanceType': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}}}},
'ModelPackage': {'AdditionalInferenceSpecifications': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}},
'InferenceSpecification': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}},
'Pipeline': {'PipelineStatus': {'Deleting'}},
'TrainingJob': {'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'SecondaryStatus': {'Pending'},
'SecondaryStatusTransitions': {'Status': {'Pending'}}},
'TrialComponent': {'SourceDetail': {'TrainingJob': {'ResourceConfig': {'InstanceGroups': {'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'InstanceType': {'ml.c6i.12xlarge',
'ml.c6i.16xlarge',
'ml.c6i.24xlarge',
'ml.c6i.2xlarge',
'ml.c6i.32xlarge',
'ml.c6i.4xlarge',
'ml.c6i.8xlarge',
'ml.c6i.xlarge',
'ml.m6i.12xlarge',
'ml.m6i.16xlarge',
'ml.m6i.24xlarge',
'ml.m6i.2xlarge',
'ml.m6i.32xlarge',
'ml.m6i.4xlarge',
'ml.m6i.8xlarge',
'ml.m6i.large',
'ml.m6i.xlarge',
'ml.p4de.24xlarge'}},
'SecondaryStatus': {'Pending'},
'SecondaryStatusTransitions': {'Status': {'Pending'}}}}}}}
Finds SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order.
You can query against the following value types: numeric, text, Boolean, and timestamp.
Note
The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information.
See also: AWS API Documentation
Request Syntax
client.search(
Resource='TrainingJob'|'Experiment'|'ExperimentTrial'|'ExperimentTrialComponent'|'Endpoint'|'Model'|'ModelPackage'|'ModelPackageGroup'|'Pipeline'|'PipelineExecution'|'FeatureGroup'|'FeatureMetadata'|'Image'|'ImageVersion'|'Project'|'HyperParameterTuningJob'|'ModelCard',
SearchExpression={
'Filters': [
{
'Name': 'string',
'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In',
'Value': 'string'
},
],
'NestedFilters': [
{
'NestedPropertyName': 'string',
'Filters': [
{
'Name': 'string',
'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In',
'Value': 'string'
},
]
},
],
'SubExpressions': [
{'... recursive ...'},
],
'Operator': 'And'|'Or'
},
SortBy='string',
SortOrder='Ascending'|'Descending',
NextToken='string',
MaxResults=123,
CrossAccountFilterOption='SameAccount'|'CrossAccount'
)
string
[REQUIRED]
The name of the SageMaker resource to search for.
dict
A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions , NestedFilters , and Filters that can be included in a SearchExpression object is 50.
Filters (list) --
A list of filter objects.
(dict) --
A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a Value , but not an Operator , SageMaker uses the equals operator.
In search, there are several property types:
Metrics
To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :
{
"Name": "Metrics.accuracy",
"Operator": "GreaterThan",
"Value": "0.9"
}
HyperParameters
To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :
{
"Name": "HyperParameters.learning_rate",
"Operator": "LessThan",
"Value": "0.5"
}
Tags
To define a tag filter, enter a value with the form Tags.<key> .
Name (string) -- [REQUIRED]
A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.
Operator (string) --
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
Equals
The value of Name equals Value .
NotEquals
The value of Name doesn't equal Value .
Exists
The Name property exists.
NotExists
The Name property does not exist.
GreaterThan
The value of Name is greater than Value . Not supported for text properties.
GreaterThanOrEqualTo
The value of Name is greater than or equal to Value . Not supported for text properties.
LessThan
The value of Name is less than Value . Not supported for text properties.
LessThanOrEqualTo
The value of Name is less than or equal to Value . Not supported for text properties.
In
The value of Name is one of the comma delimited strings in Value . Only supported for text properties.
Contains
The value of Name contains the string Value . Only supported for text properties.
A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:
Experiment.DisplayName
Experiment.ExperimentName
Experiment.Tags
Trial.DisplayName
Trial.TrialName
Trial.Tags
TrialComponent.DisplayName
TrialComponent.TrialComponentName
TrialComponent.Tags
TrialComponent.InputArtifacts
TrialComponent.OutputArtifacts
A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded. "
Value (string) --
A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .
NestedFilters (list) --
A list of nested filter objects.
(dict) --
A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API.
For example, to filter on a training job's InputDataConfig property with a specific channel name and S3Uri prefix, define the following filters:
'{Name:"InputDataConfig.ChannelName", "Operator":"Equals", "Value":"train"}',
'{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"Contains", "Value":"mybucket/catdata"}'
NestedPropertyName (string) -- [REQUIRED]
The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig .
Filters (list) -- [REQUIRED]
A list of filters. Each filter acts on a property. Filters must contain at least one Filters value. For example, a NestedFilters call might include a filter on the PropertyName parameter of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri .
(dict) --
A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API.
If you specify a Value , but not an Operator , SageMaker uses the equals operator.
In search, there are several property types:
Metrics
To define a metric filter, enter a value using the form "Metrics.<name>" , where <name> is a metric name. For example, the following filter searches for training jobs with an "accuracy" metric greater than "0.9" :
{
"Name": "Metrics.accuracy",
"Operator": "GreaterThan",
"Value": "0.9"
}
HyperParameters
To define a hyperparameter filter, enter a value with the form "HyperParameters.<name>" . Decimal hyperparameter values are treated as a decimal in a comparison if the specified Value is also a decimal value. If the specified Value is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a "learning_rate" hyperparameter that is less than "0.5" :
{
"Name": "HyperParameters.learning_rate",
"Operator": "LessThan",
"Value": "0.5"
}
Tags
To define a tag filter, enter a value with the form Tags.<key> .
Name (string) -- [REQUIRED]
A resource property name. For example, TrainingJobName . For valid property names, see SearchRecord . You must specify a valid property for the resource.
Operator (string) --
A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:
Equals
The value of Name equals Value .
NotEquals
The value of Name doesn't equal Value .
Exists
The Name property exists.
NotExists
The Name property does not exist.
GreaterThan
The value of Name is greater than Value . Not supported for text properties.
GreaterThanOrEqualTo
The value of Name is greater than or equal to Value . Not supported for text properties.
LessThan
The value of Name is less than Value . Not supported for text properties.
LessThanOrEqualTo
The value of Name is less than or equal to Value . Not supported for text properties.
In
The value of Name is one of the comma delimited strings in Value . Only supported for text properties.
Contains
The value of Name contains the string Value . Only supported for text properties.
A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:
Experiment.DisplayName
Experiment.ExperimentName
Experiment.Tags
Trial.DisplayName
Trial.TrialName
Trial.Tags
TrialComponent.DisplayName
TrialComponent.TrialComponentName
TrialComponent.Tags
TrialComponent.InputArtifacts
TrialComponent.OutputArtifacts
A SearchExpression can include only one Contains operator for all other values of Name . In these cases, if you include multiple Contains operators in the SearchExpression , the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded. "
Value (string) --
A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS .
SubExpressions (list) --
A list of search expression objects.
(dict) --
A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A SearchExpression can contain up to twenty elements.
A SearchExpression contains the following components:
A list of Filter objects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value.
A list of NestedFilter objects. Each nested filter defines a list of Boolean expressions using a list of resource properties. A nested filter is satisfied if a single object in the list satisfies all Boolean expressions.
A list of SearchExpression objects. A search expression object can be nested in a list of search expression objects.
A Boolean operator: And or Or .
Operator (string) --
A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify And . If only a single conditional statement needs to be true for the entire search expression to be true, specify Or . The default value is And .
string
The name of the resource property used to sort the SearchResults . The default is LastModifiedTime .
string
How SearchResults are ordered. Valid values are Ascending or Descending . The default is Descending .
string
If more than MaxResults resources match the specified SearchExpression , the response includes a NextToken . The NextToken can be passed to the next SearchRequest to continue retrieving results.
integer
The maximum number of results to return.
string
A cross account filter option. When the value is "CrossAccount" the search results will only include resources made discoverable to you from other accounts. When the value is "SameAccount" or null the search results will only include resources from your account. Default is null . For more information on searching for resources made discoverable to your account, see Search discoverable resources in the SageMaker Developer Guide. The maximum number of ResourceCatalog s viewable is 1000.
dict
Response Syntax
{
'Results': [
{
'TrainingJob': {
'TrainingJobName': 'string',
'TrainingJobArn': 'string',
'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'ModelArtifacts': {
'S3ModelArtifacts': 'string'
},
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting'|'Pending',
'FailureReason': 'string',
'HyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'AlgorithmName': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False,
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'TrainingImageConfig': {
'TrainingRepositoryAccessMode': 'Platform'|'Vpc',
'TrainingRepositoryAuthConfig': {
'TrainingRepositoryCredentialsProviderArn': 'string'
}
}
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
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'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
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'InstanceGroupName': 'string'
},
]
},
'VpcConfig': {
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],
'Subnets': [
'string',
]
},
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'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
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{
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'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': 'string'
},
],
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{
'MetricName': 'string',
'Value': ...,
'Timestamp': datetime(2015, 1, 1)
},
],
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
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'LocalPath': 'string'
},
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'BillableTimeInSeconds': 123,
'DebugHookConfig': {
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'S3OutputPath': 'string',
'HookParameters': {
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},
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{
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'CollectionParameters': {
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}
},
]
},
'ExperimentConfig': {
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'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
},
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{
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'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
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'RuleParameters': {
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}
},
],
'TensorBoardOutputConfig': {
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'S3OutputPath': 'string'
},
'DebugRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'ProfilerConfig': {
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'ProfilingIntervalInMilliseconds': 123,
'ProfilingParameters': {
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},
'DisableProfiler': True|False
},
'Environment': {
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},
'RetryStrategy': {
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},
'Tags': [
{
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'Value': 'string'
},
]
},
'Experiment': {
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'DisplayName': 'string',
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'SourceType': 'string'
},
'Description': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
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},
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},
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{
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},
]
},
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'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
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'UserProfileName': 'string',
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'SourceIdentity': 'string'
}
},
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}
},
'MetadataProperties': {
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'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'Tags': [
{
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'Value': 'string'
},
],
'TrialComponentSummaries': [
{
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'TrialComponentSource': {
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'SourceType': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
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'UserProfileName': 'string',
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}
}
},
]
},
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'Source': {
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'SourceType': 'string'
},
'Status': {
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},
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'EndTime': datetime(2015, 1, 1),
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}
},
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'SourceIdentity': 'string'
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},
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}
},
'InputArtifacts': {
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'Value': 'string'
}
},
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'Value': 'string'
}
},
'Metrics': [
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'SourceArn': 'string',
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'Min': 123.0,
'Last': 123.0,
'Count': 123,
'Avg': 123.0,
'StdDev': 123.0
},
],
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},
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'TuningJobArn': 'string',
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
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},
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},
'AlgorithmSpecification': {
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'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
],
'EnableSageMakerMetricsTimeSeries': True|False,
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'TrainingImageConfig': {
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'TrainingRepositoryAuthConfig': {
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}
}
},
'RoleArn': 'string',
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{
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'DataSource': {
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'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
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'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
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'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'OutputDataConfig': {
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'S3OutputPath': 'string',
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},
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'InstanceCount': 123,
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string',
'KeepAlivePeriodInSeconds': 123,
'InstanceGroups': [
{
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'InstanceCount': 123,
'InstanceGroupName': 'string'
},
]
},
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123,
'MaxWaitTimeInSeconds': 123,
'MaxPendingTimeInSeconds': 123
},
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'SecondaryStatusTransitions': [
{
'Status': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting'|'Pending',
'StartTime': datetime(2015, 1, 1),
'EndTime': datetime(2015, 1, 1),
'StatusMessage': 'string'
},
],
'FinalMetricDataList': [
{
'MetricName': 'string',
'Value': ...,
'Timestamp': datetime(2015, 1, 1)
},
],
'EnableNetworkIsolation': True|False,
'EnableInterContainerTrafficEncryption': True|False,
'EnableManagedSpotTraining': True|False,
'CheckpointConfig': {
'S3Uri': 'string',
'LocalPath': 'string'
},
'TrainingTimeInSeconds': 123,
'BillableTimeInSeconds': 123,
'DebugHookConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string',
'HookParameters': {
'string': 'string'
},
'CollectionConfigurations': [
{
'CollectionName': 'string',
'CollectionParameters': {
'string': 'string'
}
},
]
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
},
'DebugRuleConfigurations': [
{
'RuleConfigurationName': 'string',
'LocalPath': 'string',
'S3OutputPath': 'string',
'RuleEvaluatorImage': 'string',
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'RuleParameters': {
'string': 'string'
}
},
],
'TensorBoardOutputConfig': {
'LocalPath': 'string',
'S3OutputPath': 'string'
},
'DebugRuleEvaluationStatuses': [
{
'RuleConfigurationName': 'string',
'RuleEvaluationJobArn': 'string',
'RuleEvaluationStatus': 'InProgress'|'NoIssuesFound'|'IssuesFound'|'Error'|'Stopping'|'Stopped',
'StatusDetails': 'string',
'LastModifiedTime': datetime(2015, 1, 1)
},
],
'ProfilerConfig': {
'S3OutputPath': 'string',
'ProfilingIntervalInMilliseconds': 123,
'ProfilingParameters': {
'string': 'string'
},
'DisableProfiler': True|False
},
'Environment': {
'string': 'string'
},
'RetryStrategy': {
'MaximumRetryAttempts': 123
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ProcessingJob': {
'ProcessingInputs': [
{
'InputName': 'string',
'AppManaged': True|False,
'S3Input': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3DataType': 'ManifestFile'|'S3Prefix',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'S3CompressionType': 'None'|'Gzip'
},
'DatasetDefinition': {
'AthenaDatasetDefinition': {
'Catalog': 'string',
'Database': 'string',
'QueryString': 'string',
'WorkGroup': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'ORC'|'AVRO'|'JSON'|'TEXTFILE',
'OutputCompression': 'GZIP'|'SNAPPY'|'ZLIB'
},
'RedshiftDatasetDefinition': {
'ClusterId': 'string',
'Database': 'string',
'DbUser': 'string',
'QueryString': 'string',
'ClusterRoleArn': 'string',
'OutputS3Uri': 'string',
'KmsKeyId': 'string',
'OutputFormat': 'PARQUET'|'CSV',
'OutputCompression': 'None'|'GZIP'|'BZIP2'|'ZSTD'|'SNAPPY'
},
'LocalPath': 'string',
'DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'InputMode': 'Pipe'|'File'
}
},
],
'ProcessingOutputConfig': {
'Outputs': [
{
'OutputName': 'string',
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
},
'FeatureStoreOutput': {
'FeatureGroupName': 'string'
},
'AppManaged': True|False
},
],
'KmsKeyId': 'string'
},
'ProcessingJobName': 'string',
'ProcessingResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'AppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
]
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string',
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
},
'ProcessingJobArn': 'string',
'ProcessingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'ExitMessage': 'string',
'FailureReason': 'string',
'ProcessingEndTime': datetime(2015, 1, 1),
'ProcessingStartTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'MonitoringScheduleArn': 'string',
'AutoMLJobArn': 'string',
'TrainingJobArn': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'TransformJob': {
'TransformJobName': 'string',
'TransformJobArn': 'string',
'TransformJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'FailureReason': 'string',
'ModelName': 'string',
'MaxConcurrentTransforms': 123,
'ModelClientConfig': {
'InvocationsTimeoutInSeconds': 123,
'InvocationsMaxRetries': 123
},
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'DataCaptureConfig': {
'DestinationS3Uri': 'string',
'KmsKeyId': 'string',
'GenerateInferenceId': True|False
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'TransformStartTime': datetime(2015, 1, 1),
'TransformEndTime': datetime(2015, 1, 1),
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'DataProcessing': {
'InputFilter': 'string',
'OutputFilter': 'string',
'JoinSource': 'Input'|'None'
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
}
},
'LineageGroupArn': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'Parents': [
{
'TrialName': 'string',
'ExperimentName': 'string'
},
],
'RunName': 'string'
},
'Endpoint': {
'EndpointName': 'string',
'EndpointArn': 'string',
'EndpointConfigName': 'string',
'ProductionVariants': [
{
'VariantName': 'string',
'DeployedImages': [
{
'SpecifiedImage': 'string',
'ResolvedImage': 'string',
'ResolutionTime': datetime(2015, 1, 1)
},
],
'CurrentWeight': ...,
'DesiredWeight': ...,
'CurrentInstanceCount': 123,
'DesiredInstanceCount': 123,
'VariantStatus': [
{
'Status': 'Creating'|'Updating'|'Deleting'|'ActivatingTraffic'|'Baking',
'StatusMessage': 'string',
'StartTime': datetime(2015, 1, 1)
},
],
'CurrentServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'DesiredServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'ManagedInstanceScaling': {
'Status': 'ENABLED'|'DISABLED',
'MinInstanceCount': 123,
'MaxInstanceCount': 123
},
'RoutingConfig': {
'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
}
},
],
'DataCaptureConfig': {
'EnableCapture': True|False,
'CaptureStatus': 'Started'|'Stopped',
'CurrentSamplingPercentage': 123,
'DestinationS3Uri': 'string',
'KmsKeyId': 'string'
},
'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed'|'UpdateRollbackFailed',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringSchedules': [
{
'MonitoringScheduleArn': 'string',
'MonitoringScheduleName': 'string',
'MonitoringScheduleStatus': 'Pending'|'Failed'|'Scheduled'|'Stopped',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringScheduleConfig': {
'ScheduleConfig': {
'ScheduleExpression': 'string',
'DataAnalysisStartTime': 'string',
'DataAnalysisEndTime': 'string'
},
'MonitoringJobDefinition': {
'BaselineConfig': {
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
},
'StatisticsResource': {
'S3Uri': 'string'
}
},
'MonitoringInputs': [
{
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string',
'ExcludeFeaturesAttribute': 'string'
},
'BatchTransformInput': {
'DataCapturedDestinationS3Uri': 'string',
'DatasetFormat': {
'Csv': {
'Header': True|False
},
'Json': {
'Line': True|False
},
'Parquet': {}
},
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string',
'ExcludeFeaturesAttribute': 'string'
}
},
],
'MonitoringOutputConfig': {
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'MonitoringResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'MonitoringAppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string'
},
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
'EndpointName': 'string',
'LastMonitoringExecutionSummary': {
'MonitoringScheduleName': 'string',
'ScheduledTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringExecutionStatus': 'Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped',
'ProcessingJobArn': 'string',
'EndpointName': 'string',
'FailureReason': 'string',
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
],
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'ShadowProductionVariants': [
{
'VariantName': 'string',
'DeployedImages': [
{
'SpecifiedImage': 'string',
'ResolvedImage': 'string',
'ResolutionTime': datetime(2015, 1, 1)
},
],
'CurrentWeight': ...,
'DesiredWeight': ...,
'CurrentInstanceCount': 123,
'DesiredInstanceCount': 123,
'VariantStatus': [
{
'Status': 'Creating'|'Updating'|'Deleting'|'ActivatingTraffic'|'Baking',
'StatusMessage': 'string',
'StartTime': datetime(2015, 1, 1)
},
],
'CurrentServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'DesiredServerlessConfig': {
'MemorySizeInMB': 123,
'MaxConcurrency': 123,
'ProvisionedConcurrency': 123
},
'ManagedInstanceScaling': {
'Status': 'ENABLED'|'DISABLED',
'MinInstanceCount': 123,
'MaxInstanceCount': 123
},
'RoutingConfig': {
'RoutingStrategy': 'LEAST_OUTSTANDING_REQUESTS'|'RANDOM'
}
},
]
},
'ModelPackage': {
'ModelPackageName': 'string',
'ModelPackageGroupName': 'string',
'ModelPackageVersion': 123,
'ModelPackageArn': 'string',
'ModelPackageDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'InferenceSpecification': {
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalS3DataSource': {
'S3DataType': 'S3Object',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
'SourceAlgorithmSpecification': {
'SourceAlgorithms': [
{
'ModelDataUrl': 'string',
'AlgorithmName': 'string'
},
]
},
'ValidationSpecification': {
'ValidationRole': 'string',
'ValidationProfiles': [
{
'ProfileName': 'string',
'TransformJobDefinition': {
'MaxConcurrentTransforms': 123,
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
}
}
},
]
},
'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting',
'ModelPackageStatusDetails': {
'ValidationStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
],
'ImageScanStatuses': [
{
'Name': 'string',
'Status': 'NotStarted'|'InProgress'|'Completed'|'Failed',
'FailureReason': 'string'
},
]
},
'CertifyForMarketplace': True|False,
'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval',
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'MetadataProperties': {
'CommitId': 'string',
'Repository': 'string',
'GeneratedBy': 'string',
'ProjectId': 'string'
},
'ModelMetrics': {
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Bias': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PreTrainingReport': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PostTrainingReport': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Report': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'ApprovalDescription': 'string',
'Domain': 'string',
'Task': 'string',
'SamplePayloadUrl': 'string',
'AdditionalInferenceSpecifications': [
{
'Name': 'string',
'Description': 'string',
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalS3DataSource': {
'S3DataType': 'S3Object',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
],
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'CustomerMetadataProperties': {
'string': 'string'
},
'DriftCheckBaselines': {
'Bias': {
'ConfigFile': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PreTrainingConstraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'PostTrainingConstraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'Explainability': {
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'ConfigFile': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
},
'ModelDataQuality': {
'Statistics': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
},
'Constraints': {
'ContentType': 'string',
'ContentDigest': 'string',
'S3Uri': 'string'
}
}
},
'SkipModelValidation': 'All'|'None'
},
'ModelPackageGroup': {
'ModelPackageGroupName': 'string',
'ModelPackageGroupArn': 'string',
'ModelPackageGroupDescription': 'string',
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'ModelPackageGroupStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'|'DeleteFailed',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Pipeline': {
'PipelineArn': 'string',
'PipelineName': 'string',
'PipelineDisplayName': 'string',
'PipelineDescription': 'string',
'RoleArn': 'string',
'PipelineStatus': 'Active'|'Deleting',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'LastRunTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'ParallelismConfiguration': {
'MaxParallelExecutionSteps': 123
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'PipelineExecution': {
'PipelineArn': 'string',
'PipelineExecutionArn': 'string',
'PipelineExecutionDisplayName': 'string',
'PipelineExecutionStatus': 'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded',
'PipelineExecutionDescription': 'string',
'PipelineExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string'
},
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'ParallelismConfiguration': {
'MaxParallelExecutionSteps': 123
},
'SelectiveExecutionConfig': {
'SourcePipelineExecutionArn': 'string',
'SelectedSteps': [
{
'StepName': 'string'
},
]
},
'PipelineParameters': [
{
'Name': 'string',
'Value': 'string'
},
]
},
'FeatureGroup': {
'FeatureGroupArn': 'string',
'FeatureGroupName': 'string',
'RecordIdentifierFeatureName': 'string',
'EventTimeFeatureName': 'string',
'FeatureDefinitions': [
{
'FeatureName': 'string',
'FeatureType': 'Integral'|'Fractional'|'String',
'CollectionType': 'List'|'Set'|'Vector',
'CollectionConfig': {
'VectorConfig': {
'Dimension': 123
}
}
},
],
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'OnlineStoreConfig': {
'SecurityConfig': {
'KmsKeyId': 'string'
},
'EnableOnlineStore': True|False,
'TtlDuration': {
'Unit': 'Seconds'|'Minutes'|'Hours'|'Days'|'Weeks',
'Value': 123
},
'StorageType': 'Standard'|'InMemory'
},
'OfflineStoreConfig': {
'S3StorageConfig': {
'S3Uri': 'string',
'KmsKeyId': 'string',
'ResolvedOutputS3Uri': 'string'
},
'DisableGlueTableCreation': True|False,
'DataCatalogConfig': {
'TableName': 'string',
'Catalog': 'string',
'Database': 'string'
},
'TableFormat': 'Default'|'Glue'|'Iceberg'
},
'RoleArn': 'string',
'FeatureGroupStatus': 'Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed',
'OfflineStoreStatus': {
'Status': 'Active'|'Blocked'|'Disabled',
'BlockedReason': 'string'
},
'LastUpdateStatus': {
'Status': 'Successful'|'Failed'|'InProgress',
'FailureReason': 'string'
},
'FailureReason': 'string',
'Description': 'string',
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'FeatureMetadata': {
'FeatureGroupArn': 'string',
'FeatureGroupName': 'string',
'FeatureName': 'string',
'FeatureType': 'Integral'|'Fractional'|'String',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'Description': 'string',
'Parameters': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'Project': {
'ProjectArn': 'string',
'ProjectName': 'string',
'ProjectId': 'string',
'ProjectDescription': 'string',
'ServiceCatalogProvisioningDetails': {
'ProductId': 'string',
'ProvisioningArtifactId': 'string',
'PathId': 'string',
'ProvisioningParameters': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ServiceCatalogProvisionedProductDetails': {
'ProvisionedProductId': 'string',
'ProvisionedProductStatusMessage': 'string'
},
'ProjectStatus': 'Pending'|'CreateInProgress'|'CreateCompleted'|'CreateFailed'|'DeleteInProgress'|'DeleteFailed'|'DeleteCompleted'|'UpdateInProgress'|'UpdateCompleted'|'UpdateFailed',
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'CreationTime': datetime(2015, 1, 1),
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
}
},
'HyperParameterTuningJob': {
'HyperParameterTuningJobName': 'string',
'HyperParameterTuningJobArn': 'string',
'HyperParameterTuningJobConfig': {
'Strategy': 'Bayesian'|'Random'|'Hyperband'|'Grid',
'StrategyConfig': {
'HyperbandStrategyConfig': {
'MinResource': 123,
'MaxResource': 123
}
},
'HyperParameterTuningJobObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'ResourceLimits': {
'MaxNumberOfTrainingJobs': 123,
'MaxParallelTrainingJobs': 123,
'MaxRuntimeInSeconds': 123
},
'ParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'TrainingJobEarlyStoppingType': 'Off'|'Auto',
'TuningJobCompletionCriteria': {
'TargetObjectiveMetricValue': ...,
'BestObjectiveNotImproving': {
'MaxNumberOfTrainingJobsNotImproving': 123
},
'ConvergenceDetected': {
'CompleteOnConvergence': 'Disabled'|'Enabled'
}
},
'RandomSeed': 123
},
'TrainingJobDefinition': {
'DefinitionName': 'string',
'TuningObjective': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string'
},
'HyperParameterRanges': {
'IntegerParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'ContinuousParameterRanges': [
{
'Name': 'string',
'MinValue': 'string',
'MaxValue': 'string',
'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic'
},
],
'CategoricalParameterRanges': [
{
'Name': 'string',
'Values': [
'string',
]
},
],
'AutoParameters': [
{
'Name': 'string',
'ValueHint': 'string'
},
]
},
'StaticHyperParameters': {
'string': 'string'
},
'AlgorithmSpecification': {
'TrainingImage': 'string',
'TrainingInputMode': 'Pipe'|'File'|'FastFile',
'AlgorithmName': 'string',
'MetricDefinitions': [
{
'Name': 'string',
'Regex': 'string'
},
]
},
'RoleArn': 'string',
'InputDataConfig': [
{
'ChannelName': 'string',
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'AttributeNames': [
'string',
],
'InstanceGroupNames': [
'string',
]
},
'FileSystemDataSource': {
'FileSystemId': 'string',
'FileSystemAccessMode': 'rw'|'ro',
'FileSystemType': 'EFS'|'FSxLustre',
'DirectoryPath': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'RecordWrapperType': 'None'|'RecordIO',
'InputMode': 'Pipe'|'File'|'FastFile',
'ShuffleConfig': {
'Seed': 123
}
},
],
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'OutputDataConfig': {
'KmsKeyId': 'string',
'S3OutputPath': 'string',
'CompressionType': 'GZIP'|'NONE'
},
'ResourceConfig': {
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'TuningJobName': 'string',
'CreationTime': datetime(2015, 1, 1),
'TrainingStartTime': datetime(2015, 1, 1),
'TrainingEndTime': datetime(2015, 1, 1),
'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'TunedHyperParameters': {
'string': 'string'
},
'FailureReason': 'string',
'FinalHyperParameterTuningJobObjectiveMetric': {
'Type': 'Maximize'|'Minimize',
'MetricName': 'string',
'Value': ...
},
'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed'
},
'WarmStartConfig': {
'ParentHyperParameterTuningJobs': [
{
'HyperParameterTuningJobName': 'string'
},
],
'WarmStartType': 'IdenticalDataAndAlgorithm'|'TransferLearning'
},
'FailureReason': 'string',
'TuningJobCompletionDetails': {
'NumberOfTrainingJobsObjectiveNotImproving': 123,
'ConvergenceDetectedTime': datetime(2015, 1, 1)
},
'ConsumedResources': {
'RuntimeInSeconds': 123
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'ModelCard': {
'ModelCardArn': 'string',
'ModelCardName': 'string',
'ModelCardVersion': 123,
'Content': 'string',
'ModelCardStatus': 'Draft'|'PendingReview'|'Approved'|'Archived',
'SecurityConfig': {
'KmsKeyId': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'ModelId': 'string',
'RiskRating': 'string',
'ModelPackageGroupName': 'string'
},
'Model': {
'Model': {
'ModelName': 'string',
'PrimaryContainer': {
'ContainerHostname': 'string',
'Image': 'string',
'ImageConfig': {
'RepositoryAccessMode': 'Platform'|'Vpc',
'RepositoryAuthConfig': {
'RepositoryCredentialsProviderArn': 'string'
}
},
'Mode': 'SingleModel'|'MultiModel',
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
}
}
},
'Environment': {
'string': 'string'
},
'ModelPackageName': 'string',
'InferenceSpecificationName': 'string',
'MultiModelConfig': {
'ModelCacheSetting': 'Enabled'|'Disabled'
}
},
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageConfig': {
'RepositoryAccessMode': 'Platform'|'Vpc',
'RepositoryAuthConfig': {
'RepositoryCredentialsProviderArn': 'string'
}
},
'Mode': 'SingleModel'|'MultiModel',
'ModelDataUrl': 'string',
'ModelDataSource': {
'S3DataSource': {
'S3Uri': 'string',
'S3DataType': 'S3Prefix'|'S3Object',
'CompressionType': 'None'|'Gzip',
'ModelAccessConfig': {
'AcceptEula': True|False
}
}
},
'Environment': {
'string': 'string'
},
'ModelPackageName': 'string',
'InferenceSpecificationName': 'string',
'MultiModelConfig': {
'ModelCacheSetting': 'Enabled'|'Disabled'
}
},
],
'InferenceExecutionConfig': {
'Mode': 'Serial'|'Direct'
},
'ExecutionRoleArn': 'string',
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
},
'CreationTime': datetime(2015, 1, 1),
'ModelArn': 'string',
'EnableNetworkIsolation': True|False,
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'DeploymentRecommendation': {
'RecommendationStatus': 'IN_PROGRESS'|'COMPLETED'|'FAILED'|'NOT_APPLICABLE',
'RealTimeInferenceRecommendations': [
{
'RecommendationId': 'string',
'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
'Environment': {
'string': 'string'
}
},
]
}
},
'Endpoints': [
{
'EndpointName': 'string',
'EndpointArn': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed'|'UpdateRollbackFailed'
},
],
'LastBatchTransformJob': {
'TransformJobName': 'string',
'TransformJobArn': 'string',
'TransformJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped',
'FailureReason': 'string',
'ModelName': 'string',
'MaxConcurrentTransforms': 123,
'ModelClientConfig': {
'InvocationsTimeoutInSeconds': 123,
'InvocationsMaxRetries': 123
},
'MaxPayloadInMB': 123,
'BatchStrategy': 'MultiRecord'|'SingleRecord',
'Environment': {
'string': 'string'
},
'TransformInput': {
'DataSource': {
'S3DataSource': {
'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
'S3Uri': 'string'
}
},
'ContentType': 'string',
'CompressionType': 'None'|'Gzip',
'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord'
},
'TransformOutput': {
'S3OutputPath': 'string',
'Accept': 'string',
'AssembleWith': 'None'|'Line',
'KmsKeyId': 'string'
},
'DataCaptureConfig': {
'DestinationS3Uri': 'string',
'KmsKeyId': 'string',
'GenerateInferenceId': True|False
},
'TransformResources': {
'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'InstanceCount': 123,
'VolumeKmsKeyId': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'TransformStartTime': datetime(2015, 1, 1),
'TransformEndTime': datetime(2015, 1, 1),
'LabelingJobArn': 'string',
'AutoMLJobArn': 'string',
'DataProcessing': {
'InputFilter': 'string',
'OutputFilter': 'string',
'JoinSource': 'Input'|'None'
},
'ExperimentConfig': {
'ExperimentName': 'string',
'TrialName': 'string',
'TrialComponentDisplayName': 'string',
'RunName': 'string'
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
},
'MonitoringSchedules': [
{
'MonitoringScheduleArn': 'string',
'MonitoringScheduleName': 'string',
'MonitoringScheduleStatus': 'Pending'|'Failed'|'Scheduled'|'Stopped',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability',
'FailureReason': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringScheduleConfig': {
'ScheduleConfig': {
'ScheduleExpression': 'string',
'DataAnalysisStartTime': 'string',
'DataAnalysisEndTime': 'string'
},
'MonitoringJobDefinition': {
'BaselineConfig': {
'BaseliningJobName': 'string',
'ConstraintsResource': {
'S3Uri': 'string'
},
'StatisticsResource': {
'S3Uri': 'string'
}
},
'MonitoringInputs': [
{
'EndpointInput': {
'EndpointName': 'string',
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string',
'ExcludeFeaturesAttribute': 'string'
},
'BatchTransformInput': {
'DataCapturedDestinationS3Uri': 'string',
'DatasetFormat': {
'Csv': {
'Header': True|False
},
'Json': {
'Line': True|False
},
'Parquet': {}
},
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string',
'ExcludeFeaturesAttribute': 'string'
}
},
],
'MonitoringOutputConfig': {
'MonitoringOutputs': [
{
'S3Output': {
'S3Uri': 'string',
'LocalPath': 'string',
'S3UploadMode': 'Continuous'|'EndOfJob'
}
},
],
'KmsKeyId': 'string'
},
'MonitoringResources': {
'ClusterConfig': {
'InstanceCount': 123,
'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
'VolumeSizeInGB': 123,
'VolumeKmsKeyId': 'string'
}
},
'MonitoringAppSpecification': {
'ImageUri': 'string',
'ContainerEntrypoint': [
'string',
],
'ContainerArguments': [
'string',
],
'RecordPreprocessorSourceUri': 'string',
'PostAnalyticsProcessorSourceUri': 'string'
},
'StoppingCondition': {
'MaxRuntimeInSeconds': 123
},
'Environment': {
'string': 'string'
},
'NetworkConfig': {
'EnableInterContainerTrafficEncryption': True|False,
'EnableNetworkIsolation': True|False,
'VpcConfig': {
'SecurityGroupIds': [
'string',
],
'Subnets': [
'string',
]
}
},
'RoleArn': 'string'
},
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
'EndpointName': 'string',
'MonitoringAlertSummaries': [
{
'MonitoringAlertName': 'string',
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'AlertStatus': 'InAlert'|'OK',
'DatapointsToAlert': 123,
'EvaluationPeriod': 123,
'Actions': {
'ModelDashboardIndicator': {
'Enabled': True|False
}
}
},
],
'LastMonitoringExecutionSummary': {
'MonitoringScheduleName': 'string',
'ScheduledTime': datetime(2015, 1, 1),
'CreationTime': datetime(2015, 1, 1),
'LastModifiedTime': datetime(2015, 1, 1),
'MonitoringExecutionStatus': 'Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped',
'ProcessingJobArn': 'string',
'EndpointName': 'string',
'FailureReason': 'string',
'MonitoringJobDefinitionName': 'string',
'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability'
},
'BatchTransformInput': {
'DataCapturedDestinationS3Uri': 'string',
'DatasetFormat': {
'Csv': {
'Header': True|False
},
'Json': {
'Line': True|False
},
'Parquet': {}
},
'LocalPath': 'string',
'S3InputMode': 'Pipe'|'File',
'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key',
'FeaturesAttribute': 'string',
'InferenceAttribute': 'string',
'ProbabilityAttribute': 'string',
'ProbabilityThresholdAttribute': 123.0,
'StartTimeOffset': 'string',
'EndTimeOffset': 'string',
'ExcludeFeaturesAttribute': 'string'
}
},
],
'ModelCard': {
'ModelCardArn': 'string',
'ModelCardName': 'string',
'ModelCardVersion': 123,
'ModelCardStatus': 'Draft'|'PendingReview'|'Approved'|'Archived',
'SecurityConfig': {
'KmsKeyId': 'string'
},
'CreationTime': datetime(2015, 1, 1),
'CreatedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'LastModifiedTime': datetime(2015, 1, 1),
'LastModifiedBy': {
'UserProfileArn': 'string',
'UserProfileName': 'string',
'DomainId': 'string',
'IamIdentity': {
'Arn': 'string',
'PrincipalId': 'string',
'SourceIdentity': 'string'
}
},
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
],
'ModelId': 'string',
'RiskRating': 'string'
}
}
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
Results (list) --
A list of SearchRecord objects.
(dict) --
A single resource returned as part of the Search API response.
TrainingJob (dict) --
The properties of a training job.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
TrainingJobStatus (string) --
The status of the training job.
Training job statuses are:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus .
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition .
SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
Warning
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
(string) --
(string) --
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker .
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
For more information, see the note in the AlgorithmName parameter description.
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage , and vice versa.
If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
You use one of the SageMaker built-in algorithms
You use one of the following Prebuilt SageMaker Docker Images :
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
ContainerEntrypoint (list) --
The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
(string) --
TrainingImageConfig (dict) --
The configuration to use an image from a private Docker registry for a training job.
TrainingRepositoryAccessMode (string) --
The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to Vpc .
TrainingRepositoryAuthConfig (dict) --
An object containing authentication information for a private Docker registry containing your training images.
TrainingRepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
RoleArn (string) --
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig (list) --
An array of Channel objects that describes each data input channel.
Your input must be in the same Amazon Web Services region as your training job.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
CreationTime (datetime) --
A timestamp that indicates when the training job was created.
TrainingStartTime (datetime) --
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime . The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
TrainingEndTime (datetime) --
Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
LastModifiedTime (datetime) --
A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions (list) --
A history of all of the secondary statuses that the training job has transitioned through.
(dict) --
An array element of SecondaryStatusTransitions for DescribeTrainingJob . It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Status (string) --
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
StartTime (datetime) --
A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime (datetime) --
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage (string) --
A detailed description of the progress within a secondary status.
SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
Warning
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob , and StatusMessage together. For example, at the start of a training job, you might see the following:
TrainingJobStatus - InProgress
SecondaryStatus - Training
StatusMessage - Downloading the training image
FinalMetricDataList (list) --
A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If the TrainingJob was created with network isolation, the value is set to true . If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training .
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds.
DebugHookConfig (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
LocalPath (string) --
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for metrics and tensors.
HookParameters (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters.
(string) --
(string) --
CollectionConfigurations (list) --
Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(dict) --
Configuration information for the Amazon SageMaker Debugger output tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
(string) --
(string) --
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
DebugRuleConfigurations (list) --
Information about the debug rule configuration.
(dict) --
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a custom rule for debugging a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
TensorBoardOutputConfig (dict) --
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Information about the evaluation status of the rules for the training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration.
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
ProfilerConfig (dict) --
Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath (string) --
Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds (integer) --
A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters (dict) --
Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig , PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(string) --
(string) --
DisableProfiler (boolean) --
Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to True .
Environment (dict) --
The environment variables to set in the Docker container.
(string) --
(string) --
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Tags (list) --
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Experiment (dict) --
The properties of an experiment.
ExperimentName (string) --
The name of the experiment.
ExperimentArn (string) --
The Amazon Resource Name (ARN) of the experiment.
DisplayName (string) --
The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
Source (dict) --
The source of the experiment.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source type.
Description (string) --
The description of the experiment.
CreationTime (datetime) --
When the experiment was created.
CreatedBy (dict) --
Who created the experiment.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedTime (datetime) --
When the experiment was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
Tags (list) --
The list of tags that are associated with the experiment. You can use Search API to search on the tags.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Trial (dict) --
The properties of a trial.
TrialName (string) --
The name of the trial.
TrialArn (string) --
The Amazon Resource Name (ARN) of the trial.
DisplayName (string) --
The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
ExperimentName (string) --
The name of the experiment the trial is part of.
Source (dict) --
The source of the trial.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the trial was created.
CreatedBy (dict) --
Who created the trial.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedTime (datetime) --
Who last modified the trial.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
Tags (list) --
The list of tags that are associated with the trial. You can use Search API to search on the tags.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
TrialComponentSummaries (list) --
A list of the components associated with the trial. For each component, a summary of the component's properties is included.
(dict) --
A short summary of a trial component.
TrialComponentName (string) --
The name of the trial component.
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
TrialComponentSource (dict) --
The Amazon Resource Name (ARN) and job type of the source of a trial component.
SourceArn (string) --
The source Amazon Resource Name (ARN).
SourceType (string) --
The source job type.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
TrialComponent (dict) --
The properties of a trial component.
TrialComponentName (string) --
The name of the trial component.
DisplayName (string) --
The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
TrialComponentArn (string) --
The Amazon Resource Name (ARN) of the trial component.
Source (dict) --
The Amazon Resource Name (ARN) and job type of the source of the component.
SourceArn (string) --
The source Amazon Resource Name (ARN).
SourceType (string) --
The source job type.
Status (dict) --
The status of the trial component.
PrimaryStatus (string) --
The status of the trial component.
Message (string) --
If the component failed, a message describing why.
StartTime (datetime) --
When the component started.
EndTime (datetime) --
When the component ended.
CreationTime (datetime) --
When the component was created.
CreatedBy (dict) --
Who created the trial component.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedTime (datetime) --
When the component was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
Parameters (dict) --
The hyperparameters of the component.
(string) --
(dict) --
The value of a hyperparameter. Only one of NumberValue or StringValue can be specified.
This object is specified in the CreateTrialComponent request.
StringValue (string) --
The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.
NumberValue (float) --
The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.
InputArtifacts (dict) --
The input artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
OutputArtifacts (dict) --
The output artifacts of the component.
(string) --
(dict) --
Represents an input or output artifact of a trial component. You specify TrialComponentArtifact as part of the InputArtifacts and OutputArtifacts parameters in the CreateTrialComponent request.
Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types. Examples of output artifacts are metrics, snapshots, logs, and images.
MediaType (string) --
The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
Value (string) --
The location of the artifact.
Metrics (list) --
The metrics for the component.
(dict) --
A summary of the metrics of a trial component.
MetricName (string) --
The name of the metric.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
TimeStamp (datetime) --
When the metric was last updated.
Max (float) --
The maximum value of the metric.
Min (float) --
The minimum value of the metric.
Last (float) --
The most recent value of the metric.
Count (integer) --
The number of samples used to generate the metric.
Avg (float) --
The average value of the metric.
StdDev (float) --
The standard deviation of the metric.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
SourceDetail (dict) --
Details of the source of the component.
SourceArn (string) --
The Amazon Resource Name (ARN) of the source.
TrainingJob (dict) --
Information about a training job that's the source of a trial component.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobArn (string) --
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the job.
ModelArtifacts (dict) --
Information about the Amazon S3 location that is configured for storing model artifacts.
S3ModelArtifacts (string) --
The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz .
TrainingJobStatus (string) --
The status of the training job.
Training job statuses are:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus .
SecondaryStatus (string) --
Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition .
SageMaker provides primary statuses and secondary statuses that apply to each of them:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
Warning
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
FailureReason (string) --
If the training job failed, the reason it failed.
HyperParameters (dict) --
Algorithm-specific parameters.
(string) --
(string) --
AlgorithmSpecification (dict) --
Information about the algorithm used for training, and algorithm metadata.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker .
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
For more information, see the note in the AlgorithmName parameter description.
AlgorithmName (string) --
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
Note
You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.
Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage , and vice versa.
If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
MetricDefinitions (list) --
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
EnableSageMakerMetricsTimeSeries (boolean) --
To generate and save time-series metrics during training, set to true . The default is false and time-series metrics aren't generated except in the following cases:
You use one of the SageMaker built-in algorithms
You use one of the following Prebuilt SageMaker Docker Images :
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
ContainerEntrypoint (list) --
The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
(string) --
TrainingImageConfig (dict) --
The configuration to use an image from a private Docker registry for a training job.
TrainingRepositoryAccessMode (string) --
The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to Vpc .
TrainingRepositoryAuthConfig (dict) --
An object containing authentication information for a private Docker registry containing your training images.
TrainingRepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
RoleArn (string) --
The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
InputDataConfig (list) --
An array of Channel objects that describes each data input channel.
Your input must be in the same Amazon Web Services region as your training job.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
OutputDataConfig (dict) --
The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
VpcConfig (dict) --
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
StoppingCondition (dict) --
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
CreationTime (datetime) --
A timestamp that indicates when the training job was created.
TrainingStartTime (datetime) --
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime . The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
TrainingEndTime (datetime) --
Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
LastModifiedTime (datetime) --
A timestamp that indicates when the status of the training job was last modified.
SecondaryStatusTransitions (list) --
A history of all of the secondary statuses that the training job has transitioned through.
(dict) --
An array element of SecondaryStatusTransitions for DescribeTrainingJob . It provides additional details about a status that the training job has transitioned through. A training job can be in one of several states, for example, starting, downloading, training, or uploading. Within each state, there are a number of intermediate states. For example, within the starting state, SageMaker could be starting the training job or launching the ML instances. These transitional states are referred to as the job's secondary status.
Status (string) --
Contains a secondary status information from a training job.
Status might be one of the following secondary statuses:
InProgress
Starting - Starting the training job.
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
Completed - The training job has completed.
Failed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse .
Stopped
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
Stopped - The training job has stopped.
Stopping
Stopping - Stopping the training job.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
StartTime (datetime) --
A timestamp that shows when the training job transitioned to the current secondary status state.
EndTime (datetime) --
A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
StatusMessage (string) --
A detailed description of the progress within a secondary status.
SageMaker provides secondary statuses and status messages that apply to each of them:
Starting
Starting the training job.
Launching requested ML instances.
Insufficient capacity error from EC2 while launching instances, retrying!
Launched instance was unhealthy, replacing it!
Preparing the instances for training.
Training
Downloading the training image.
Training image download completed. Training in progress.
Warning
Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.
To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob , and StatusMessage together. For example, at the start of a training job, you might see the following:
TrainingJobStatus - InProgress
SecondaryStatus - Training
StatusMessage - Downloading the training image
FinalMetricDataList (list) --
A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
(dict) --
The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.
MetricName (string) --
The name of the metric.
Value (float) --
The value of the metric.
Timestamp (datetime) --
The date and time that the algorithm emitted the metric.
EnableNetworkIsolation (boolean) --
If the TrainingJob was created with network isolation, the value is set to true . If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training .
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
TrainingTimeInSeconds (integer) --
The training time in seconds.
BillableTimeInSeconds (integer) --
The billable time in seconds.
DebugHookConfig (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
LocalPath (string) --
Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for metrics and tensors.
HookParameters (dict) --
Configuration information for the Amazon SageMaker Debugger hook parameters.
(string) --
(string) --
CollectionConfigurations (list) --
Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(dict) --
Configuration information for the Amazon SageMaker Debugger output tensor collections.
CollectionName (string) --
The name of the tensor collection. The name must be unique relative to other rule configuration names.
CollectionParameters (dict) --
Parameter values for the tensor collection. The allowed parameters are "name" , "include_regex" , "reduction_config" , "save_config" , "tensor_names" , and "save_histogram" .
(string) --
(string) --
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
DebugRuleConfigurations (list) --
Information about the debug rule configuration.
(dict) --
Configuration information for SageMaker Debugger rules for debugging. To learn more about how to configure the DebugRuleConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
RuleConfigurationName (string) --
The name of the rule configuration. It must be unique relative to other rule configuration names.
LocalPath (string) --
Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/ .
S3OutputPath (string) --
Path to Amazon S3 storage location for rules.
RuleEvaluatorImage (string) --
The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
InstanceType (string) --
The instance type to deploy a custom rule for debugging a training job.
VolumeSizeInGB (integer) --
The size, in GB, of the ML storage volume attached to the processing instance.
RuleParameters (dict) --
Runtime configuration for rule container.
(string) --
(string) --
TensorBoardOutputConfig (dict) --
Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
LocalPath (string) --
Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard .
S3OutputPath (string) --
Path to Amazon S3 storage location for TensorBoard output.
DebugRuleEvaluationStatuses (list) --
Information about the evaluation status of the rules for the training job.
(dict) --
Information about the status of the rule evaluation.
RuleConfigurationName (string) --
The name of the rule configuration.
RuleEvaluationJobArn (string) --
The Amazon Resource Name (ARN) of the rule evaluation job.
RuleEvaluationStatus (string) --
Status of the rule evaluation.
StatusDetails (string) --
Details from the rule evaluation.
LastModifiedTime (datetime) --
Timestamp when the rule evaluation status was last modified.
ProfilerConfig (dict) --
Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
S3OutputPath (string) --
Path to Amazon S3 storage location for system and framework metrics.
ProfilingIntervalInMilliseconds (integer) --
A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
ProfilingParameters (dict) --
Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig , PythonProfilingConfig , and DataLoaderProfilingConfig . The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job .
(string) --
(string) --
DisableProfiler (boolean) --
Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to True .
Environment (dict) --
The environment variables to set in the Docker container.
(string) --
(string) --
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Tags (list) --
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ProcessingJob (dict) --
Information about a processing job that's the source of a trial component.
ProcessingInputs (list) --
List of input configurations for the processing job.
(dict) --
The inputs for a processing job. The processing input must specify exactly one of either S3Input or DatasetDefinition types.
InputName (string) --
The name for the processing job input.
AppManaged (boolean) --
When True , input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.
S3Input (dict) --
Configuration for downloading input data from Amazon S3 into the processing container.
S3Uri (string) --
The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
LocalPath (string) --
The local path in your container where you want Amazon SageMaker to write input data to. LocalPath is an absolute path to the input data and must begin with /opt/ml/processing/ . LocalPath is a required parameter when AppManaged is False (default).
S3DataType (string) --
Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.
S3InputMode (string) --
Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume.
S3DataDistributionType (string) --
Whether to distribute the data from Amazon S3 to all processing instances with FullyReplicated , or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance.
S3CompressionType (string) --
Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container. Gzip can only be used when Pipe mode is specified as the S3InputMode . In Pipe mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume.
DatasetDefinition (dict) --
Configuration for a Dataset Definition input.
AthenaDatasetDefinition (dict) --
Configuration for Athena Dataset Definition input.
Catalog (string) --
The name of the data catalog used in Athena query execution.
Database (string) --
The name of the database used in the Athena query execution.
QueryString (string) --
The SQL query statements, to be executed.
WorkGroup (string) --
The name of the workgroup in which the Athena query is being started.
OutputS3Uri (string) --
The location in Amazon S3 where Athena query results are stored.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
OutputFormat (string) --
The data storage format for Athena query results.
OutputCompression (string) --
The compression used for Athena query results.
RedshiftDatasetDefinition (dict) --
Configuration for Redshift Dataset Definition input.
ClusterId (string) --
The Redshift cluster Identifier.
Database (string) --
The name of the Redshift database used in Redshift query execution.
DbUser (string) --
The database user name used in Redshift query execution.
QueryString (string) --
The SQL query statements to be executed.
ClusterRoleArn (string) --
The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
OutputS3Uri (string) --
The location in Amazon S3 where the Redshift query results are stored.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
OutputFormat (string) --
The data storage format for Redshift query results.
OutputCompression (string) --
The compression used for Redshift query results.
LocalPath (string) --
The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
DataDistributionType (string) --
Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
InputMode (string) --
Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
ProcessingOutputConfig (dict) --
Configuration for uploading output from the processing container.
Outputs (list) --
An array of outputs configuring the data to upload from the processing container.
(dict) --
Describes the results of a processing job. The processing output must specify exactly one of either S3Output or FeatureStoreOutput types.
OutputName (string) --
The name for the processing job output.
S3Output (dict) --
Configuration for processing job outputs in Amazon S3.
S3Uri (string) --
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
LocalPath (string) --
The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3. LocalPath is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.
S3UploadMode (string) --
Whether to upload the results of the processing job continuously or after the job completes.
FeatureStoreOutput (dict) --
Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified.
FeatureGroupName (string) --
The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
AppManaged (boolean) --
When True , output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.
ProcessingJobName (string) --
The name of the processing job.
ProcessingResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the resources in a cluster used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.
Note
Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage.
For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
StoppingCondition (dict) --
Configures conditions under which the processing job should be stopped, such as how long the processing job has been running. After the condition is met, the processing job is stopped.
MaxRuntimeInSeconds (integer) --
Specifies the maximum runtime in seconds.
AppSpecification (dict) --
Configuration to run a processing job in a specified container image.
ImageUri (string) --
The container image to be run by the processing job.
ContainerEntrypoint (list) --
The entrypoint for a container used to run a processing job.
(string) --
ContainerArguments (list) --
The arguments for a container used to run a processing job.
(string) --
Environment (dict) --
Sets the environment variables in the Docker container.
(string) --
(string) --
NetworkConfig (dict) --
Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
RoleArn (string) --
The ARN of the role used to create the processing job.
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
ProcessingJobArn (string) --
The ARN of the processing job.
ProcessingJobStatus (string) --
The status of the processing job.
ExitMessage (string) --
A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
FailureReason (string) --
A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
ProcessingEndTime (datetime) --
The time that the processing job ended.
ProcessingStartTime (datetime) --
The time that the processing job started.
LastModifiedTime (datetime) --
The time the processing job was last modified.
CreationTime (datetime) --
The time the processing job was created.
MonitoringScheduleArn (string) --
The ARN of a monitoring schedule for an endpoint associated with this processing job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.
TrainingJobArn (string) --
The ARN of the training job associated with this processing job.
Tags (list) --
An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
TransformJob (dict) --
Information about a transform job that's the source of a trial component.
TransformJobName (string) --
The name of the transform job.
TransformJobArn (string) --
The Amazon Resource Name (ARN) of the transform job.
TransformJobStatus (string) --
The status of the transform job.
Transform job statuses are:
InProgress - The job is in progress.
Completed - The job has completed.
Failed - The transform job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTransformJob call.
Stopping - The transform job is stopping.
Stopped - The transform job has stopped.
FailureReason (string) --
If the transform job failed, the reason it failed.
ModelName (string) --
The name of the model associated with the transform job.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms .
ModelClientConfig (dict) --
Configures the timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds (integer) --
The timeout value in seconds for an invocation request. The default value is 600.
InvocationsMaxRetries (integer) --
The maximum number of retries when invocation requests are failing. The default value is 3.
MaxPayloadInMB (integer) --
The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.
BatchStrategy (string) --
Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
Describes the input source of a transform job and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Describes the results of a transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
DataCaptureConfig (dict) --
Configuration to control how SageMaker captures inference data for batch transform jobs.
DestinationS3Uri (string) --
The Amazon S3 location being used to capture the data.
KmsKeyId (string) --
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the batch transform job.
The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
GenerateInferenceId (boolean) --
Flag that indicates whether to append inference id to the output.
TransformResources (dict) --
Describes the resources, including ML instance types and ML instance count, to use for transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
CreationTime (datetime) --
A timestamp that shows when the transform Job was created.
TransformStartTime (datetime) --
Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime .
TransformEndTime (datetime) --
Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime .
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job that created the transform job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
DataProcessing (dict) --
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records .
InputFilter (string) --
A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want SageMaker to pass the entire input dataset to the algorithm, accept the default value $ .
Examples: "$" , "$[1:]" , "$.features"
OutputFilter (string) --
A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default value, $ . If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.
Examples: "$" , "$[0,5:]" , "$['id','SageMakerOutput']"
JoinSource (string) --
Specifies the source of the data to join with the transformed data. The valid values are None and Input . The default value is None , which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input . You can specify OutputFilter as an additional filter to select a portion of the joined dataset and store it in the output file.
For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput . The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput .
For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records .
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
Tags (list) --
A list of tags associated with the transform job.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
LineageGroupArn (string) --
The Amazon Resource Name (ARN) of the lineage group resource.
Tags (list) --
The list of tags that are associated with the component. You can use Search API to search on the tags.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Parents (list) --
An array of the parents of the component. A parent is a trial the component is associated with and the experiment the trial is part of. A component might not have any parents.
(dict) --
The trial that a trial component is associated with and the experiment the trial is part of. A component might not be associated with a trial. A component can be associated with multiple trials.
TrialName (string) --
The name of the trial.
ExperimentName (string) --
The name of the experiment.
RunName (string) --
The name of the experiment run.
Endpoint (dict) --
A hosted endpoint for real-time inference.
EndpointName (string) --
The name of the endpoint.
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
EndpointConfigName (string) --
The endpoint configuration associated with the endpoint.
ProductionVariants (list) --
A list of the production variants hosted on the endpoint. Each production variant is a model.
(dict) --
Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating , you get different desired and current values.
VariantName (string) --
The name of the variant.
DeployedImages (list) --
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .
(dict) --
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .
If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .
SpecifiedImage (string) --
The image path you specified when you created the model.
ResolvedImage (string) --
The specific digest path of the image hosted in this ProductionVariant .
ResolutionTime (datetime) --
The date and time when the image path for the model resolved to the ResolvedImage
CurrentWeight (float) --
The weight associated with the variant.
DesiredWeight (float) --
The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
CurrentInstanceCount (integer) --
The number of instances associated with the variant.
DesiredInstanceCount (integer) --
The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
VariantStatus (list) --
The endpoint variant status which describes the current deployment stage status or operational status.
(dict) --
Describes the status of the production variant.
Status (string) --
The endpoint variant status which describes the current deployment stage status or operational status.
Creating : Creating inference resources for the production variant.
Deleting : Terminating inference resources for the production variant.
Updating : Updating capacity for the production variant.
ActivatingTraffic : Turning on traffic for the production variant.
Baking : Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
StatusMessage (string) --
A message that describes the status of the production variant.
StartTime (datetime) --
The start time of the current status change.
CurrentServerlessConfig (dict) --
The serverless configuration for the endpoint.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
DesiredServerlessConfig (dict) --
The serverless configuration requested for the endpoint update.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
ManagedInstanceScaling (dict) --
Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status (string) --
Indicates whether managed instance scaling is enabled.
MinInstanceCount (integer) --
The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount (integer) --
The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig (dict) --
Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy (string) --
Sets how the endpoint routes incoming traffic:
LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.
RANDOM : The endpoint routes each request to a randomly chosen instance.
DataCaptureConfig (dict) --
The currently active data capture configuration used by your Endpoint.
EnableCapture (boolean) --
Whether data capture is enabled or disabled.
CaptureStatus (string) --
Whether data capture is currently functional.
CurrentSamplingPercentage (integer) --
The percentage of requests being captured by your Endpoint.
DestinationS3Uri (string) --
The Amazon S3 location being used to capture the data.
KmsKeyId (string) --
The KMS key being used to encrypt the data in Amazon S3.
EndpointStatus (string) --
The status of the endpoint.
FailureReason (string) --
If the endpoint failed, the reason it failed.
CreationTime (datetime) --
The time that the endpoint was created.
LastModifiedTime (datetime) --
The last time the endpoint was modified.
MonitoringSchedules (list) --
A list of monitoring schedules for the endpoint. For information about model monitoring, see Amazon SageMaker Model Monitor .
(dict) --
A schedule for a model monitoring job. For information about model monitor, see Amazon SageMaker Model Monitor .
MonitoringScheduleArn (string) --
The Amazon Resource Name (ARN) of the monitoring schedule.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
MonitoringScheduleStatus (string) --
The status of the monitoring schedule. This can be one of the following values.
PENDING - The schedule is pending being created.
FAILED - The schedule failed.
SCHEDULED - The schedule was successfully created.
STOPPED - The schedule was stopped.
MonitoringType (string) --
The type of the monitoring job definition to schedule.
FailureReason (string) --
If the monitoring schedule failed, the reason it failed.
CreationTime (datetime) --
The time that the monitoring schedule was created.
LastModifiedTime (datetime) --
The last time the monitoring schedule was changed.
MonitoringScheduleConfig (dict) --
Configures the monitoring schedule and defines the monitoring job.
ScheduleConfig (dict) --
Configures the monitoring schedule.
ScheduleExpression (string) --
A cron expression that describes details about the monitoring schedule.
The supported cron expressions are:
If you want to set the job to start every hour, use the following: Hourly: cron(0 * ? * * *)
If you want to start the job daily: cron(0 [00-23] ? * * *)
If you want to run the job one time, immediately, use the following keyword: NOW
For example, the following are valid cron expressions:
Daily at noon UTC: cron(0 12 ? * * *)
Daily at midnight UTC: cron(0 0 ? * * *)
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)
Every two hours starting at midnight: cron(0 0/2 ? * * *)
Note
Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.
We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.
You can also specify the keyword NOW to run the monitoring job immediately, one time, without recurring.
DataAnalysisStartTime (string) --
Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify: "-PT5H" .
The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the DataAnalysisEndTime parameter.
If you set ScheduleExpression to NOW , this parameter is required.
DataAnalysisEndTime (string) --
Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify: "-PT1H" .
The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the DataAnalysisStartTime parameter.
If you set ScheduleExpression to NOW , this parameter is required.
MonitoringJobDefinition (dict) --
Defines the monitoring job.
BaselineConfig (dict) --
Baseline configuration used to validate that the data conforms to the specified constraints and statistics
BaseliningJobName (string) --
The name of the job that performs baselining for the monitoring job.
ConstraintsResource (dict) --
The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
S3Uri (string) --
The Amazon S3 URI for the constraints resource.
StatisticsResource (dict) --
The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
S3Uri (string) --
The Amazon S3 URI for the statistics resource.
MonitoringInputs (list) --
The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
(dict) --
The inputs for a monitoring job.
EndpointInput (dict) --
The endpoint for a monitoring job.
EndpointName (string) --
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
LocalPath (string) --
Path to the filesystem where the endpoint data is available to the container.
S3InputMode (string) --
Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
S3DataDistributionType (string) --
Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to FullyReplicated
FeaturesAttribute (string) --
The attributes of the input data that are the input features.
InferenceAttribute (string) --
The attribute of the input data that represents the ground truth label.
ProbabilityAttribute (string) --
In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute (float) --
The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset (string) --
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
EndTimeOffset (string) --
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
ExcludeFeaturesAttribute (string) --
The attributes of the input data to exclude from the analysis.
BatchTransformInput (dict) --
Input object for the batch transform job.
DataCapturedDestinationS3Uri (string) --
The Amazon S3 location being used to capture the data.
DatasetFormat (dict) --
The dataset format for your batch transform job.
Csv (dict) --
The CSV dataset used in the monitoring job.
Header (boolean) --
Indicates if the CSV data has a header.
Json (dict) --
The JSON dataset used in the monitoring job
Line (boolean) --
Indicates if the file should be read as a JSON object per line.
Parquet (dict) --
The Parquet dataset used in the monitoring job
LocalPath (string) --
Path to the filesystem where the batch transform data is available to the container.
S3InputMode (string) --
Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
S3DataDistributionType (string) --
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
FeaturesAttribute (string) --
The attributes of the input data that are the input features.
InferenceAttribute (string) --
The attribute of the input data that represents the ground truth label.
ProbabilityAttribute (string) --
In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute (float) --
The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset (string) --
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
EndTimeOffset (string) --
If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
ExcludeFeaturesAttribute (string) --
The attributes of the input data to exclude from the analysis.
MonitoringOutputConfig (dict) --
The array of outputs from the monitoring job to be uploaded to Amazon S3.
MonitoringOutputs (list) --
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
(dict) --
The output object for a monitoring job.
S3Output (dict) --
The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri (string) --
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath (string) --
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode (string) --
Whether to upload the results of the monitoring job continuously or after the job completes.
KmsKeyId (string) --
The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the cluster resources used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId (string) --
The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
MonitoringAppSpecification (dict) --
Configures the monitoring job to run a specified Docker container image.
ImageUri (string) --
The container image to be run by the monitoring job.
ContainerEntrypoint (list) --
Specifies the entrypoint for a container used to run the monitoring job.
(string) --
ContainerArguments (list) --
An array of arguments for the container used to run the monitoring job.
(string) --
RecordPreprocessorSourceUri (string) --
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri (string) --
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
StoppingCondition (dict) --
Specifies a time limit for how long the monitoring job is allowed to run.
MaxRuntimeInSeconds (integer) --
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Environment (dict) --
Sets the environment variables in the Docker container.
(string) --
(string) --
NetworkConfig (dict) --
Specifies networking options for an monitoring job.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
RoleArn (string) --
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringJobDefinitionName (string) --
The name of the monitoring job definition to schedule.
MonitoringType (string) --
The type of the monitoring job definition to schedule.
EndpointName (string) --
The endpoint that hosts the model being monitored.
LastMonitoringExecutionSummary (dict) --
Summary of information about the last monitoring job to run.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
ScheduledTime (datetime) --
The time the monitoring job was scheduled.
CreationTime (datetime) --
The time at which the monitoring job was created.
LastModifiedTime (datetime) --
A timestamp that indicates the last time the monitoring job was modified.
MonitoringExecutionStatus (string) --
The status of the monitoring job.
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
EndpointName (string) --
The name of the endpoint used to run the monitoring job.
FailureReason (string) --
Contains the reason a monitoring job failed, if it failed.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringType (string) --
The type of the monitoring job.
Tags (list) --
A list of the tags associated with the monitoring schedlue. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Tags (list) --
A list of the tags associated with the endpoint. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ShadowProductionVariants (list) --
A list of the shadow variants hosted on the endpoint. Each shadow variant is a model in shadow mode with production traffic replicated from the production variant.
(dict) --
Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating , you get different desired and current values.
VariantName (string) --
The name of the variant.
DeployedImages (list) --
An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant .
(dict) --
Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant .
If you used the registry/repository[:tag] form to specify the image path of the primary container when you created the model hosted in this ProductionVariant , the path resolves to a path of the form registry/repository[@digest] . A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide .
SpecifiedImage (string) --
The image path you specified when you created the model.
ResolvedImage (string) --
The specific digest path of the image hosted in this ProductionVariant .
ResolutionTime (datetime) --
The date and time when the image path for the model resolved to the ResolvedImage
CurrentWeight (float) --
The weight associated with the variant.
DesiredWeight (float) --
The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.
CurrentInstanceCount (integer) --
The number of instances associated with the variant.
DesiredInstanceCount (integer) --
The number of instances requested in the UpdateEndpointWeightsAndCapacities request.
VariantStatus (list) --
The endpoint variant status which describes the current deployment stage status or operational status.
(dict) --
Describes the status of the production variant.
Status (string) --
The endpoint variant status which describes the current deployment stage status or operational status.
Creating : Creating inference resources for the production variant.
Deleting : Terminating inference resources for the production variant.
Updating : Updating capacity for the production variant.
ActivatingTraffic : Turning on traffic for the production variant.
Baking : Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.
StatusMessage (string) --
A message that describes the status of the production variant.
StartTime (datetime) --
The start time of the current status change.
CurrentServerlessConfig (dict) --
The serverless configuration for the endpoint.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
DesiredServerlessConfig (dict) --
The serverless configuration requested for the endpoint update.
MemorySizeInMB (integer) --
The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
MaxConcurrency (integer) --
The maximum number of concurrent invocations your serverless endpoint can process.
ProvisionedConcurrency (integer) --
The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency .
Note
This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs .
ManagedInstanceScaling (dict) --
Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.
Status (string) --
Indicates whether managed instance scaling is enabled.
MinInstanceCount (integer) --
The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
MaxInstanceCount (integer) --
The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
RoutingConfig (dict) --
Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
RoutingStrategy (string) --
Sets how the endpoint routes incoming traffic:
LEAST_OUTSTANDING_REQUESTS : The endpoint routes requests to the specific instances that have more capacity to process them.
RANDOM : The endpoint routes each request to a randomly chosen instance.
ModelPackage (dict) --
A versioned model that can be deployed for SageMaker inference.
ModelPackageName (string) --
The name of the model.
ModelPackageGroupName (string) --
The model group to which the model belongs.
ModelPackageVersion (integer) --
The version number of a versioned model.
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model package.
ModelPackageDescription (string) --
The description of the model package.
CreationTime (datetime) --
The time that the model package was created.
InferenceSpecification (dict) --
Defines how to perform inference generation after a training job is run.
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) --
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) --
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) --
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
This parameter is required for unversioned models, and optional for versioned models.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
SourceAlgorithmSpecification (dict) --
A list of algorithms that were used to create a model package.
SourceAlgorithms (list) --
A list of the algorithms that were used to create a model package.
(dict) --
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same Amazon Web Services region as the algorithm.
AlgorithmName (string) --
The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
ValidationSpecification (dict) --
Specifies batch transform jobs that SageMaker runs to validate your model package.
ValidationRole (string) --
The IAM roles to be used for the validation of the model package.
ValidationProfiles (list) --
An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.
(dict) --
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace.
ProfileName (string) --
The name of the profile for the model package.
TransformJobDefinition (dict) --
The TransformJobDefinition object that describes the transform job used for the validation of the model package.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
MaxPayloadInMB (integer) --
The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
BatchStrategy (string) --
A string that determines the number of records included in a single mini-batch.
SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
A description of the input source and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
TransformResources (dict) --
Identifies the ML compute instances for the transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
ModelPackageStatus (string) --
The status of the model package. This can be one of the following values.
PENDING - The model package is pending being created.
IN_PROGRESS - The model package is in the process of being created.
COMPLETED - The model package was successfully created.
FAILED - The model package failed.
DELETING - The model package is in the process of being deleted.
ModelPackageStatusDetails (dict) --
Specifies the validation and image scan statuses of the model package.
ValidationStatuses (list) --
The validation status of the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
ImageScanStatuses (list) --
The status of the scan of the Docker image container for the model package.
(dict) --
Represents the overall status of a model package.
Name (string) --
The name of the model package for which the overall status is being reported.
Status (string) --
The current status.
FailureReason (string) --
if the overall status is Failed , the reason for the failure.
CertifyForMarketplace (boolean) --
Whether the model package is to be certified to be listed on Amazon Web Services Marketplace. For information about listing model packages on Amazon Web Services Marketplace, see List Your Algorithm or Model Package on Amazon Web Services Marketplace .
ModelApprovalStatus (string) --
The approval status of the model. This can be one of the following values.
APPROVED - The model is approved
REJECTED - The model is rejected.
PENDING_MANUAL_APPROVAL - The model is waiting for manual approval.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
MetadataProperties (dict) --
Metadata properties of the tracking entity, trial, or trial component.
CommitId (string) --
The commit ID.
Repository (string) --
The repository.
GeneratedBy (string) --
The entity this entity was generated by.
ProjectId (string) --
The project ID.
ModelMetrics (dict) --
Metrics for the model.
ModelQuality (dict) --
Metrics that measure the quality of a model.
Statistics (dict) --
Model quality statistics.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
Model quality constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
ModelDataQuality (dict) --
Metrics that measure the quality of the input data for a model.
Statistics (dict) --
Data quality statistics for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
Data quality constraints for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Bias (dict) --
Metrics that measure bais in a model.
Report (dict) --
The bias report for a model
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
PreTrainingReport (dict) --
The pre-training bias report for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
PostTrainingReport (dict) --
The post-training bias report for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Explainability (dict) --
Metrics that help explain a model.
Report (dict) --
The explainability report for a model.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
LastModifiedTime (datetime) --
The last time the model package was modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
ApprovalDescription (string) --
A description provided when the model approval is set.
Domain (string) --
The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
Task (string) --
The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.
SamplePayloadUrl (string) --
The Amazon Simple Storage Service path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
AdditionalInferenceSpecifications (list) --
An array of additional Inference Specification objects.
(dict) --
A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package
Name (string) --
A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
Description (string) --
A description of the additional Inference specification
Containers (list) --
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) --
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) --
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) --
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) --
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
Tags (list) --
A list of the tags associated with the model package. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
CustomerMetadataProperties (dict) --
The metadata properties for the model package.
(string) --
(string) --
DriftCheckBaselines (dict) --
Represents the drift check baselines that can be used when the model monitor is set using the model package.
Bias (dict) --
Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
ConfigFile (dict) --
The bias config file for a model.
ContentType (string) --
The type of content stored in the file source.
ContentDigest (string) --
The digest of the file source.
S3Uri (string) --
The Amazon S3 URI for the file source.
PreTrainingConstraints (dict) --
The pre-training constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
PostTrainingConstraints (dict) --
The post-training constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Explainability (dict) --
Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.
Constraints (dict) --
The drift check explainability constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
ConfigFile (dict) --
The explainability config file for the model.
ContentType (string) --
The type of content stored in the file source.
ContentDigest (string) --
The digest of the file source.
S3Uri (string) --
The Amazon S3 URI for the file source.
ModelQuality (dict) --
Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
Statistics (dict) --
The drift check model quality statistics.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
The drift check model quality constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
ModelDataQuality (dict) --
Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.
Statistics (dict) --
The drift check model data quality statistics.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
Constraints (dict) --
The drift check model data quality constraints.
ContentType (string) --
The metric source content type.
ContentDigest (string) --
The hash key used for the metrics source.
S3Uri (string) --
The S3 URI for the metrics source.
SkipModelValidation (string) --
Indicates if you want to skip model validation.
ModelPackageGroup (dict) --
A group of versioned models in the model registry.
ModelPackageGroupName (string) --
The name of the model group.
ModelPackageGroupArn (string) --
The Amazon Resource Name (ARN) of the model group.
ModelPackageGroupDescription (string) --
The description for the model group.
CreationTime (datetime) --
The time that the model group was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
ModelPackageGroupStatus (string) --
The status of the model group. This can be one of the following values.
PENDING - The model group is pending being created.
IN_PROGRESS - The model group is in the process of being created.
COMPLETED - The model group was successfully created.
FAILED - The model group failed.
DELETING - The model group is in the process of being deleted.
DELETE_FAILED - SageMaker failed to delete the model group.
Tags (list) --
A list of the tags associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
Pipeline (dict) --
A SageMaker Model Building Pipeline instance.
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline.
PipelineName (string) --
The name of the pipeline.
PipelineDisplayName (string) --
The display name of the pipeline.
PipelineDescription (string) --
The description of the pipeline.
RoleArn (string) --
The Amazon Resource Name (ARN) of the role that created the pipeline.
PipelineStatus (string) --
The status of the pipeline.
CreationTime (datetime) --
The creation time of the pipeline.
LastModifiedTime (datetime) --
The time that the pipeline was last modified.
LastRunTime (datetime) --
The time when the pipeline was last run.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
ParallelismConfiguration (dict) --
The parallelism configuration applied to the pipeline.
MaxParallelExecutionSteps (integer) --
The max number of steps that can be executed in parallel.
Tags (list) --
A list of tags that apply to the pipeline.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
PipelineExecution (dict) --
An execution of a pipeline.
PipelineArn (string) --
The Amazon Resource Name (ARN) of the pipeline that was executed.
PipelineExecutionArn (string) --
The Amazon Resource Name (ARN) of the pipeline execution.
PipelineExecutionDisplayName (string) --
The display name of the pipeline execution.
PipelineExecutionStatus (string) --
The status of the pipeline status.
PipelineExecutionDescription (string) --
The description of the pipeline execution.
PipelineExperimentConfig (dict) --
Specifies the names of the experiment and trial created by a pipeline.
ExperimentName (string) --
The name of the experiment.
TrialName (string) --
The name of the trial.
FailureReason (string) --
If the execution failed, a message describing why.
CreationTime (datetime) --
The creation time of the pipeline execution.
LastModifiedTime (datetime) --
The time that the pipeline execution was last modified.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
ParallelismConfiguration (dict) --
The parallelism configuration applied to the pipeline execution.
MaxParallelExecutionSteps (integer) --
The max number of steps that can be executed in parallel.
SelectiveExecutionConfig (dict) --
The selective execution configuration applied to the pipeline run.
SourcePipelineExecutionArn (string) --
The ARN from a reference execution of the current pipeline. Used to copy input collaterals needed for the selected steps to run. The execution status of the pipeline can be either Failed or Success .
This field is required if the steps you specify for SelectedSteps depend on output collaterals from any non-specified pipeline steps. For more information, see Selective Execution for Pipeline Steps .
SelectedSteps (list) --
A list of pipeline steps to run. All step(s) in all path(s) between two selected steps should be included.
(dict) --
A step selected to run in selective execution mode.
StepName (string) --
The name of the pipeline step.
PipelineParameters (list) --
Contains a list of pipeline parameters. This list can be empty.
(dict) --
Assigns a value to a named Pipeline parameter.
Name (string) --
The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
Value (string) --
The literal value for the parameter.
FeatureGroup (dict) --
Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.
FeatureGroupArn (string) --
The Amazon Resource Name (ARN) of a FeatureGroup .
FeatureGroupName (string) --
The name of the FeatureGroup .
RecordIdentifierFeatureName (string) --
The name of the Feature whose value uniquely identifies a Record defined in the FeatureGroup FeatureDefinitions .
EventTimeFeatureName (string) --
The name of the feature that stores the EventTime of a Record in a FeatureGroup .
A EventTime is point in time when a new event occurs that corresponds to the creation or update of a Record in FeatureGroup . All Records in the FeatureGroup must have a corresponding EventTime .
FeatureDefinitions (list) --
A list of Feature s. Each Feature must include a FeatureName and a FeatureType .
Valid FeatureType s are Integral , Fractional and String .
FeatureName s cannot be any of the following: is_deleted , write_time , api_invocation_time .
You can create up to 2,500 FeatureDefinition s per FeatureGroup .
(dict) --
A list of features. You must include FeatureName and FeatureType . Valid feature FeatureType s are Integral , Fractional and String .
FeatureName (string) --
The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted , write_time , api_invocation_time .
FeatureType (string) --
The value type of a feature. Valid values are Integral, Fractional, or String.
CollectionType (string) --
A grouping of elements where each element within the collection must have the same feature type (String , Integral , or Fractional ).
List : An ordered collection of elements.
Set : An unordered collection of unique elements.
Vector : A specialized list that represents a fixed-size array of elements. The vector dimension is determined by you. Must have elements with fractional feature types.
CollectionConfig (dict) --
Configuration for your collection.
VectorConfig (dict) --
Configuration for your vector collection type.
Dimension : The number of elements in your vector.
Dimension (integer) --
The number of elements in your vector.
CreationTime (datetime) --
The time a FeatureGroup was created.
LastModifiedTime (datetime) --
A timestamp indicating the last time you updated the feature group.
OnlineStoreConfig (dict) --
Use this to specify the Amazon Web Services Key Management Service (KMS) Key ID, or KMSKeyId , for at rest data encryption. You can turn OnlineStore on or off by specifying the EnableOnlineStore flag at General Assembly.
The default value is False .
SecurityConfig (dict) --
Use to specify KMS Key ID (KMSKeyId ) for at-rest encryption of your OnlineStore .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key ARN that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption.
The caller (either user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId :
"kms:Encrypt"
"kms:Decrypt"
"kms:DescribeKey"
"kms:CreateGrant"
"kms:RetireGrant"
"kms:ReEncryptFrom"
"kms:ReEncryptTo"
"kms:GenerateDataKey"
"kms:ListAliases"
"kms:ListGrants"
"kms:RevokeGrant"
The caller (either user or IAM role) to all DataPlane operations (PutRecord , GetRecord , DeleteRecord ) must have the following permissions to the KmsKeyId :
"kms:Decrypt"
EnableOnlineStore (boolean) --
Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.
The default value is False .
TtlDuration (dict) --
Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration . For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.
Unit (string) --
TtlDuration time unit.
Value (integer) --
TtlDuration time value.
StorageType (string) --
Option for different tiers of low latency storage for real-time data retrieval.
Standard : A managed low latency data store for feature groups.
InMemory : A managed data store for feature groups that supports very low latency retrieval.
OfflineStoreConfig (dict) --
The configuration of an OfflineStore .
Provide an OfflineStoreConfig in a request to CreateFeatureGroup to create an OfflineStore .
To encrypt an OfflineStore using at rest data encryption, specify Amazon Web Services Key Management Service (KMS) key ID, or KMSKeyId , in S3StorageConfig .
S3StorageConfig (dict) --
The Amazon Simple Storage (Amazon S3) location of OfflineStore .
S3Uri (string) --
The S3 URI, or location in Amazon S3, of OfflineStore .
S3 URIs have a format similar to the following: s3://example-bucket/prefix/ .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key ARN of the key used to encrypt any objects written into the OfflineStore S3 location.
The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId :
"kms:GenerateDataKey"
ResolvedOutputS3Uri (string) --
The S3 path where offline records are written.
DisableGlueTableCreation (boolean) --
Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore . If set to False , Feature Store will name the OfflineStore Glue table following Athena's naming recommendations .
The default value is False .
DataCatalogConfig (dict) --
The meta data of the Glue table that is autogenerated when an OfflineStore is created.
TableName (string) --
The name of the Glue table.
Catalog (string) --
The name of the Glue table catalog.
Database (string) --
The name of the Glue table database.
TableFormat (string) --
Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg .
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM execution role used to create the feature group.
FeatureGroupStatus (string) --
A FeatureGroup status.
OfflineStoreStatus (dict) --
The status of OfflineStore .
Status (string) --
An OfflineStore status.
BlockedReason (string) --
The justification for why the OfflineStoreStatus is Blocked (if applicable).
LastUpdateStatus (dict) --
A value that indicates whether the feature group was updated successfully.
Status (string) --
A value that indicates whether the update was made successful.
FailureReason (string) --
If the update wasn't successful, indicates the reason why it failed.
FailureReason (string) --
The reason that the FeatureGroup failed to be replicated in the OfflineStore . This is failure may be due to a failure to create a FeatureGroup in or delete a FeatureGroup from the OfflineStore .
Description (string) --
A free form description of a FeatureGroup .
Tags (list) --
Tags used to define a FeatureGroup .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
FeatureMetadata (dict) --
The feature metadata used to search through the features.
FeatureGroupArn (string) --
The Amazon Resource Number (ARN) of the feature group.
FeatureGroupName (string) --
The name of the feature group containing the feature.
FeatureName (string) --
The name of feature.
FeatureType (string) --
The data type of the feature.
CreationTime (datetime) --
A timestamp indicating when the feature was created.
LastModifiedTime (datetime) --
A timestamp indicating when the feature was last modified.
Description (string) --
An optional description that you specify to better describe the feature.
Parameters (list) --
Optional key-value pairs that you specify to better describe the feature.
(dict) --
A key-value pair that you specify to describe the feature.
Key (string) --
A key that must contain a value to describe the feature.
Value (string) --
The value that belongs to a key.
Project (dict) --
The properties of a project.
ProjectArn (string) --
The Amazon Resource Name (ARN) of the project.
ProjectName (string) --
The name of the project.
ProjectId (string) --
The ID of the project.
ProjectDescription (string) --
The description of the project.
ServiceCatalogProvisioningDetails (dict) --
Details that you specify to provision a service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog .
ProductId (string) --
The ID of the product to provision.
ProvisioningArtifactId (string) --
The ID of the provisioning artifact.
PathId (string) --
The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path.
ProvisioningParameters (list) --
A list of key value pairs that you specify when you provision a product.
(dict) --
A key value pair used when you provision a project as a service catalog product. For information, see What is Amazon Web Services Service Catalog .
Key (string) --
The key that identifies a provisioning parameter.
Value (string) --
The value of the provisioning parameter.
ServiceCatalogProvisionedProductDetails (dict) --
Details of a provisioned service catalog product. For information about service catalog, see What is Amazon Web Services Service Catalog .
ProvisionedProductId (string) --
The ID of the provisioned product.
ProvisionedProductStatusMessage (string) --
The current status of the product.
AVAILABLE - Stable state, ready to perform any operation. The most recent operation succeeded and completed.
UNDER_CHANGE - Transitive state. Operations performed might not have valid results. Wait for an AVAILABLE status before performing operations.
TAINTED - Stable state, ready to perform any operation. The stack has completed the requested operation but is not exactly what was requested. For example, a request to update to a new version failed and the stack rolled back to the current version.
ERROR - An unexpected error occurred. The provisioned product exists but the stack is not running. For example, CloudFormation received a parameter value that was not valid and could not launch the stack.
PLAN_IN_PROGRESS - Transitive state. The plan operations were performed to provision a new product, but resources have not yet been created. After reviewing the list of resources to be created, execute the plan. Wait for an AVAILABLE status before performing operations.
ProjectStatus (string) --
The status of the project.
CreatedBy (dict) --
Who created the project.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
CreationTime (datetime) --
A timestamp specifying when the project was created.
Tags (list) --
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
LastModifiedTime (datetime) --
A timestamp container for when the project was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
HyperParameterTuningJob (dict) --
The properties of a hyperparameter tuning job.
HyperParameterTuningJobName (string) --
The name of a hyperparameter tuning job.
HyperParameterTuningJobArn (string) --
The Amazon Resource Name (ARN) of a hyperparameter tuning job.
HyperParameterTuningJobConfig (dict) --
Configures a hyperparameter tuning job.
Strategy (string) --
Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works .
StrategyConfig (dict) --
The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig .
HyperbandStrategyConfig (dict) --
The configuration for the object that specifies the Hyperband strategy. This parameter is only supported for the Hyperband selection for Strategy within the HyperParameterTuningJobConfig API.
MinResource (integer) --
The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job is not stopped by Hyperband .
MaxResource (integer) --
The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTrainingJ attempts to infer MaxResource from the following keys (if present) in StaticsHyperParameters :
epochs
numepochs
n-epochs
n_epochs
num_epochs
If HyperbandStrategyConfig is unable to infer a value for MaxResource , it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions . For distributive training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely.
HyperParameterTuningJobObjective (dict) --
The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
ResourceLimits (dict) --
The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
MaxNumberOfTrainingJobs (integer) --
The maximum number of training jobs that a hyperparameter tuning job can launch.
MaxParallelTrainingJobs (integer) --
The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
MaxRuntimeInSeconds (integer) --
The maximum time in seconds that a hyperparameter tuning job can run.
ParameterRanges (dict) --
The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) --
The name of the hyperparameter to search.
MinValue (string) --
The minimum value of the hyperparameter to search.
MaxValue (string) --
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) --
The name of the continuous hyperparameter to tune.
MinValue (string) --
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) --
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) --
The name of the categorical hyperparameter to tune.
Values (list) --
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) --
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) --
An example value of the hyperparameter to optimize using Autotune.
TrainingJobEarlyStoppingType (string) --
Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband . This parameter can take on one of the following values (the default value is OFF ):
OFF
Training jobs launched by the hyperparameter tuning job do not use early stopping.
AUTO
SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early .
TuningJobCompletionCriteria (dict) --
The tuning job's completion criteria.
TargetObjectiveMetricValue (float) --
The value of the objective metric.
BestObjectiveNotImproving (dict) --
A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.
MaxNumberOfTrainingJobsNotImproving (integer) --
The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
ConvergenceDetected (dict) --
A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.
CompleteOnConvergence (string) --
A flag to stop a tuning job once AMT has detected that the job has converged.
RandomSeed (integer) --
A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.
TrainingJobDefinition (dict) --
Defines the training jobs launched by a hyperparameter tuning job.
DefinitionName (string) --
The job definition name.
TuningObjective (dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables .
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
HyperParameterRanges (dict) --
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) --
The name of the hyperparameter to search.
MinValue (string) --
The minimum value of the hyperparameter to search.
MaxValue (string) --
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) --
The name of the continuous hyperparameter to tune.
MinValue (string) --
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) --
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) --
The name of the categorical hyperparameter to tune.
Values (list) --
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) --
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) --
An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters (dict) --
Specifies the values of hyperparameters that do not change for the tuning job.
(string) --
(string) --
AlgorithmSpecification (dict) --
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
AlgorithmName (string) --
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
MetricDefinitions (list) --
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig (list) --
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
VpcConfig (dict) --
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
OutputDataConfig (dict) --
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
Note
If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
HyperParameterTuningResourceConfig (dict) --
The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
InstanceType (string) --
The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
InstanceCount (integer) --
The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.
VolumeSizeInGB (integer) --
The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.
Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .
Note
SageMaker supports only the General Purpose SSD (gp2) storage volume type.
VolumeKmsKeyId (string) --
A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
AllocationStrategy (string) --
The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
InstanceConfigs (list) --
A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.
Note
If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .
(dict) --
The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
InstanceType (string) --
The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .
InstanceCount (integer) --
The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
VolumeSizeInGB (integer) --
The volume size in GB of the data to be processed for hyperparameter optimization (optional).
StoppingCondition (dict) --
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
EnableNetworkIsolation (boolean) --
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Environment (dict) --
An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note
The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
(string) --
(string) --
TrainingJobDefinitions (list) --
The job definitions included in a hyperparameter tuning job.
(dict) --
Defines the training jobs launched by a hyperparameter tuning job.
DefinitionName (string) --
The job definition name.
TuningObjective (dict) --
Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter. If you want to define a custom objective metric, see Define metrics and environment variables .
Type (string) --
Whether to minimize or maximize the objective metric.
MetricName (string) --
The name of the metric to use for the objective metric.
HyperParameterRanges (dict) --
Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches. The hyperparameter tuning job launches training jobs with hyperparameter values within these ranges to find the combination of values that result in the training job with the best performance as measured by the objective metric of the hyperparameter tuning job.
Note
The maximum number of items specified for Array Members refers to the maximum number of hyperparameters for each range and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of hyperparameters for all the ranges can't exceed the maximum number specified.
IntegerParameterRanges (list) --
The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
(dict) --
For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.
Name (string) --
The name of the hyperparameter to search.
MinValue (string) --
The minimum value of the hyperparameter to search.
MaxValue (string) --
The maximum value of the hyperparameter to search.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ContinuousParameterRanges (list) --
The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of continuous hyperparameters to tune.
Name (string) --
The name of the continuous hyperparameter to tune.
MinValue (string) --
The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValue for tuning.
MaxValue (string) --
The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
ScalingType (string) --
The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling . One of the following values:
Auto
SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.
Linear
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Logarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have only values greater than 0.
ReverseLogarithmic
Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0<=x<1.0.
CategoricalParameterRanges (list) --
The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
(dict) --
A list of categorical hyperparameters to tune.
Name (string) --
The name of the categorical hyperparameter to tune.
Values (list) --
A list of the categories for the hyperparameter.
(string) --
AutoParameters (list) --
A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
(dict) --
The name and an example value of the hyperparameter that you want to use in Autotune. If Automatic model tuning (AMT) determines that your hyperparameter is eligible for Autotune, an optimal hyperparameter range is selected for you.
Name (string) --
The name of the hyperparameter to optimize using Autotune.
ValueHint (string) --
An example value of the hyperparameter to optimize using Autotune.
StaticHyperParameters (dict) --
Specifies the values of hyperparameters that do not change for the tuning job.
(string) --
(string) --
AlgorithmSpecification (dict) --
The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
TrainingImage (string) --
The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters . SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
TrainingInputMode (string) --
The training input mode that the algorithm supports. For more information about input modes, see Algorithms .
Pipe mode
If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.
File mode
If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.
FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.
AlgorithmName (string) --
The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage .
MetricDefinitions (list) --
An array of MetricDefinition objects that specify the metrics that the algorithm emits.
(dict) --
Specifies a metric that the training algorithm writes to stderr or stdout . You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the HyperParameterTrainingJobDefinition API to evaluate job performance during hyperparameter tuning.
Name (string) --
The name of the metric.
Regex (string) --
A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables .
RoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
InputDataConfig (list) --
An array of Channel objects that specify the input for the training jobs that the tuning job launches.
(dict) --
A channel is a named input source that training algorithms can consume.
ChannelName (string) --
The name of the channel.
DataSource (dict) --
The location of the channel data.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.
If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe .
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix
A manifest might look like this: s3://bucketname/example.manifest A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri . Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets. The following code example shows a valid manifest format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] This JSON is equivalent to the following S3Uri list: s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.
Your input bucket must be located in same Amazon Web Services region as your training job.
S3DataDistributionType (string) --
If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated .
If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key . If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.
Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.
In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key . If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File ), this copies 1/n of the number of objects.
AttributeNames (list) --
A list of one or more attribute names to use that are found in a specified augmented manifest file.
(string) --
InstanceGroupNames (list) --
A list of names of instance groups that get data from the S3 data source.
(string) --
FileSystemDataSource (dict) --
The file system that is associated with a channel.
FileSystemId (string) --
The file system id.
FileSystemAccessMode (string) --
The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
FileSystemType (string) --
The file system type.
DirectoryPath (string) --
The full path to the directory to associate with the channel.
ContentType (string) --
The MIME type of the data.
CompressionType (string) --
If training data is compressed, the compression type. The default value is None . CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
RecordWrapperType (string) --
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO .
In File mode, leave this field unset or set it to None.
InputMode (string) --
(Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode , SageMaker uses the value set for TrainingInputMode . Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
To use a model for incremental training, choose File input model.
ShuffleConfig (dict) --
A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType , this shuffles the results of the S3 key prefix matches. If you use ManifestFile , the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile , the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key , the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
Seed (integer) --
Determines the shuffling order in ShuffleConfig value.
VpcConfig (dict) --
The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
OutputDataConfig (dict) --
Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
// KMS Key Alias "alias/ExampleAlias"
// Amazon Resource Name (ARN) of a KMS Key Alias "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"
If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt . If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide . If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone
The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob , CreateTransformJob , or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
S3OutputPath (string) --
Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix .
CompressionType (string) --
The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
ResourceConfig (dict) --
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
Note
If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead.
InstanceType (string) --
The ML compute instance type.
Note
SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.
Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge ) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.
US East (N. Virginia) (us-east-1)
US West (Oregon) (us-west-2)
To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team.
InstanceCount (integer) --
The number of ML compute instances to use. For distributed training, provide a value greater than 1.
VolumeSizeInGB (integer) --
The size of the ML storage volume that you want to provision.
ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.
When using an ML instance with NVMe SSD volumes , SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d , ml.g4dn , and ml.g5 .
When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2 .
To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types .
To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs .
VolumeKmsKeyId (string) --
The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be in any of the following formats:
// KMS Key ID "1234abcd-12ab-34cd-56ef-1234567890ab"
// Amazon Resource Name (ARN) of a KMS Key "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
KeepAlivePeriodInSeconds (integer) --
The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
InstanceGroups (list) --
The configuration of a heterogeneous cluster in JSON format.
(dict) --
Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups .
InstanceType (string) --
Specifies the instance type of the instance group.
InstanceCount (integer) --
Specifies the number of instances of the instance group.
InstanceGroupName (string) --
Specifies the name of the instance group.
HyperParameterTuningResourceConfig (dict) --
The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
InstanceType (string) --
The instance type used to run hyperparameter optimization tuning jobs. See descriptions of instance types for more information.
InstanceCount (integer) --
The number of compute instances of type InstanceType to use. For distributed training , select a value greater than 1.
VolumeSizeInGB (integer) --
The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified.
Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes .
Note
SageMaker supports only the General Purpose SSD (gp2) storage volume type.
VolumeKmsKeyId (string) --
A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key.
KMS Key ID:
"1234abcd-12ab-34cd-56ef-1234567890ab"
Amazon Resource Name (ARN) of a KMS key:
"arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId . For a list of instance types that use local storage, see instance store volumes . For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
AllocationStrategy (string) --
The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
InstanceConfigs (list) --
A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.
Note
If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs . Instead, use InstanceType , VolumeSizeInGB and InstanceCount . If you use InstanceConfigs , do not provide values for InstanceType , VolumeSizeInGB or InstanceCount .
(dict) --
The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job. These resources include compute instances and storage volumes. Specify one or more compute instance configurations and allocation strategies to select resources (optional).
InstanceType (string) --
The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions .
InstanceCount (integer) --
The number of instances of the type specified by InstanceType . Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
VolumeSizeInGB (integer) --
The volume size in GB of the data to be processed for hyperparameter optimization (optional).
StoppingCondition (dict) --
Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
MaxRuntimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can run before it is stopped.
For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model.
For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days.
The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
MaxWaitTimeInSeconds (integer) --
The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds . If the job does not complete during this time, SageMaker ends the job.
When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
MaxPendingTimeInSeconds (integer) --
The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
EnableNetworkIsolation (boolean) --
Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
EnableInterContainerTrafficEncryption (boolean) --
To encrypt all communications between ML compute instances in distributed training, choose True . Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
EnableManagedSpotTraining (boolean) --
A Boolean indicating whether managed spot training is enabled (True ) or not (False ).
CheckpointConfig (dict) --
Contains information about the output location for managed spot training checkpoint data.
S3Uri (string) --
Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix .
LocalPath (string) --
(Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/ .
RetryStrategy (dict) --
The number of times to retry the job when the job fails due to an InternalServerError .
MaximumRetryAttempts (integer) --
The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING .
Environment (dict) --
An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.
Note
The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified.
(string) --
(string) --
HyperParameterTuningJobStatus (string) --
The status of a hyperparameter tuning job.
CreationTime (datetime) --
The time that a hyperparameter tuning job was created.
HyperParameterTuningEndTime (datetime) --
The time that a hyperparameter tuning job ended.
LastModifiedTime (datetime) --
The time that a hyperparameter tuning job was last modified.
TrainingJobStatusCounters (dict) --
The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.
Completed (integer) --
The number of completed training jobs launched by the hyperparameter tuning job.
InProgress (integer) --
The number of in-progress training jobs launched by a hyperparameter tuning job.
RetryableError (integer) --
The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
NonRetryableError (integer) --
The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
Stopped (integer) --
The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
ObjectiveStatusCounters (dict) --
Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.
Succeeded (integer) --
The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending (integer) --
The number of training jobs that are in progress and pending evaluation of their final objective metric.
Failed (integer) --
The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
BestTrainingJob (dict) --
The container for the summary information about a training job.
TrainingJobDefinitionName (string) --
The training job definition name.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobName (string) --
The HyperParameter tuning job that launched the training job.
CreationTime (datetime) --
The date and time that the training job was created.
TrainingStartTime (datetime) --
The date and time that the training job started.
TrainingEndTime (datetime) --
Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
TrainingJobStatus (string) --
The status of the training job.
TunedHyperParameters (dict) --
A list of the hyperparameters for which you specified ranges to search.
(string) --
(string) --
FailureReason (string) --
The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric (dict) --
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type (string) --
Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
MetricName (string) --
The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables .
Value (float) --
The value of the objective metric.
ObjectiveStatus (string) --
The status of the objective metric for the training job:
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending: The training job is in progress and evaluation of its final objective metric is pending.
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
OverallBestTrainingJob (dict) --
The container for the summary information about a training job.
TrainingJobDefinitionName (string) --
The training job definition name.
TrainingJobName (string) --
The name of the training job.
TrainingJobArn (string) --
The Amazon Resource Name (ARN) of the training job.
TuningJobName (string) --
The HyperParameter tuning job that launched the training job.
CreationTime (datetime) --
The date and time that the training job was created.
TrainingStartTime (datetime) --
The date and time that the training job started.
TrainingEndTime (datetime) --
Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
TrainingJobStatus (string) --
The status of the training job.
TunedHyperParameters (dict) --
A list of the hyperparameters for which you specified ranges to search.
(string) --
(string) --
FailureReason (string) --
The reason that the training job failed.
FinalHyperParameterTuningJobObjectiveMetric (dict) --
The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
Type (string) --
Select if you want to minimize or maximize the objective metric during hyperparameter tuning.
MetricName (string) --
The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables .
Value (float) --
The value of the objective metric.
ObjectiveStatus (string) --
The status of the objective metric for the training job:
Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
Pending: The training job is in progress and evaluation of its final objective metric is pending.
Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
WarmStartConfig (dict) --
Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric, and the training job that performs the best is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.
Note
All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.
ParentHyperParameterTuningJobs (list) --
An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point .
Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
(dict) --
A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
HyperParameterTuningJobName (string) --
The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
WarmStartType (string) --
Specifies one of the following:
IDENTICAL_DATA_AND_ALGORITHM
The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
TRANSFER_LEARNING
The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
FailureReason (string) --
The error that was created when a hyperparameter tuning job failed.
TuningJobCompletionDetails (dict) --
Information about either a current or completed hyperparameter tuning job.
NumberOfTrainingJobsObjectiveNotImproving (integer) --
The number of training jobs launched by a tuning job that are not improving (1% or less) as measured by model performance evaluated against an objective function.
ConvergenceDetectedTime (datetime) --
The time in timestamp format that AMT detected model convergence, as defined by a lack of significant improvement over time based on criteria developed over a wide range of diverse benchmarking tests.
ConsumedResources (dict) --
The total amount of resources consumed by a hyperparameter tuning job.
RuntimeInSeconds (integer) --
The wall clock runtime in seconds used by your hyperparameter tuning job.
Tags (list) --
The tags associated with a hyperparameter tuning job. For more information see Tagging Amazon Web Services resources .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ModelCard (dict) --
An Amazon SageMaker Model Card that documents details about a machine learning model.
ModelCardArn (string) --
The Amazon Resource Name (ARN) of the model card.
ModelCardName (string) --
The unique name of the model card.
ModelCardVersion (integer) --
The version of the model card.
Content (string) --
The content of the model card. Content uses the model card JSON schema and provided as a string.
ModelCardStatus (string) --
The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.
Draft : The model card is a work in progress.
PendingReview : The model card is pending review.
Approved : The model card is approved.
Archived : The model card is archived. No more updates should be made to the model card, but it can still be exported.
SecurityConfig (dict) --
The security configuration used to protect model card data.
KmsKeyId (string) --
A Key Management Service key ID to use for encrypting a model card.
CreationTime (datetime) --
The date and time that the model card was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedTime (datetime) --
The date and time that the model card was last modified.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
Tags (list) --
Key-value pairs used to manage metadata for the model card.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ModelId (string) --
The unique name (ID) of the model.
RiskRating (string) --
The risk rating of the model. Different organizations might have different criteria for model card risk ratings. For more information, see Risk ratings .
ModelPackageGroupName (string) --
The model package group that contains the model package. Only relevant for model cards created for model packages in the Amazon SageMaker Model Registry.
Model (dict) --
A model displayed in the Amazon SageMaker Model Dashboard.
Model (dict) --
A model displayed in the Model Dashboard.
ModelName (string) --
The name of the model.
PrimaryContainer (dict) --
Describes the container, as part of model definition.
ContainerHostname (string) --
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline . If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
Image (string) --
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
ImageConfig (dict) --
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers .
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
RepositoryAccessMode (string) --
Set this to one of the following values:
Platform - The model image is hosted in Amazon ECR.
Vpc - The model image is hosted in a private Docker registry in your VPC.
RepositoryAuthConfig (dict) --
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
RepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Mode (string) --
Whether the container hosts a single model or multiple models.
ModelDataUrl (string) --
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters .
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
ModelDataSource (dict) --
Specifies the location of ML model data to deploy.
Note
Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) --
Specifies the S3 path of ML model data to deploy.
S3DataType (string) --
Specifies the type of ML model data to deploy.
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) --
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot (. )
A double dot (.. )
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) --
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelPackageName (string) --
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName (string) --
The inference specification name in the model package version.
MultiModelConfig (dict) --
Specifies additional configuration for multi-model endpoints.
ModelCacheSetting (string) --
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
Containers (list) --
The containers in the inference pipeline.
(dict) --
Describes the container, as part of model definition.
ContainerHostname (string) --
This parameter is ignored for models that contain only a PrimaryContainer .
When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline . If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
Image (string) --
The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
ImageConfig (dict) --
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers .
Note
The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.
RepositoryAccessMode (string) --
Set this to one of the following values:
Platform - The model image is hosted in Amazon ECR.
Vpc - The model image is hosted in a private Docker registry in your VPC.
RepositoryAuthConfig (dict) --
(Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
RepositoryCredentialsProviderArn (string) --
The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide .
Mode (string) --
Whether the container hosts a single model or multiple models.
ModelDataUrl (string) --
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters .
Note
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide .
Warning
If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl .
ModelDataSource (dict) --
Specifies the location of ML model data to deploy.
Note
Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.
S3DataSource (dict) --
Specifies the S3 location of ML model data to deploy.
S3Uri (string) --
Specifies the S3 path of ML model data to deploy.
S3DataType (string) --
Specifies the type of ML model data to deploy.
If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).
If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.
CompressionType (string) --
Specifies how the ML model data is prepared.
If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.
If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.
If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.
If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:
If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
Do not use any of the following as file names or directory names:
An empty or blank string
A string which contains null bytes
A string longer than 255 bytes
A single dot (. )
A double dot (.. )
Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.
ModelAccessConfig (dict) --
Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
AcceptEula (boolean) --
Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelPackageName (string) --
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
InferenceSpecificationName (string) --
The inference specification name in the model package version.
MultiModelConfig (dict) --
Specifies additional configuration for multi-model endpoints.
ModelCacheSetting (string) --
Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled .
InferenceExecutionConfig (dict) --
Specifies details about how containers in a multi-container endpoint are run.
Mode (string) --
How containers in a multi-container are run. The following values are valid.
SERIAL - Containers run as a serial pipeline.
DIRECT - Only the individual container that you specify is run.
ExecutionRoleArn (string) --
The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
VpcConfig (dict) --
Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
CreationTime (datetime) --
A timestamp that indicates when the model was created.
ModelArn (string) --
The Amazon Resource Name (ARN) of the model.
EnableNetworkIsolation (boolean) --
Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
Tags (list) --
A list of key-value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide .
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
DeploymentRecommendation (dict) --
A set of recommended deployment configurations for the model.
RecommendationStatus (string) --
Status of the deployment recommendation. The status NOT_APPLICABLE means that SageMaker is unable to provide a default recommendation for the model using the information provided. If the deployment status is IN_PROGRESS , retry your API call after a few seconds to get a COMPLETED deployment recommendation.
RealTimeInferenceRecommendations (list) --
A list of RealTimeInferenceRecommendation items.
(dict) --
The recommended configuration to use for Real-Time Inference.
RecommendationId (string) --
The recommendation ID which uniquely identifies each recommendation.
InstanceType (string) --
The recommended instance type for Real-Time Inference.
Environment (dict) --
The recommended environment variables to set in the model container for Real-Time Inference.
(string) --
(string) --
Endpoints (list) --
The endpoints that host a model.
(dict) --
An endpoint that hosts a model displayed in the Amazon SageMaker Model Dashboard.
EndpointName (string) --
The endpoint name.
EndpointArn (string) --
The Amazon Resource Name (ARN) of the endpoint.
CreationTime (datetime) --
A timestamp that indicates when the endpoint was created.
LastModifiedTime (datetime) --
The last time the endpoint was modified.
EndpointStatus (string) --
The endpoint status.
LastBatchTransformJob (dict) --
A batch transform job. For information about SageMaker batch transform, see Use Batch Transform .
TransformJobName (string) --
The name of the transform job.
TransformJobArn (string) --
The Amazon Resource Name (ARN) of the transform job.
TransformJobStatus (string) --
The status of the transform job.
Transform job statuses are:
InProgress - The job is in progress.
Completed - The job has completed.
Failed - The transform job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTransformJob call.
Stopping - The transform job is stopping.
Stopped - The transform job has stopped.
FailureReason (string) --
If the transform job failed, the reason it failed.
ModelName (string) --
The name of the model associated with the transform job.
MaxConcurrentTransforms (integer) --
The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms .
ModelClientConfig (dict) --
Configures the timeout and maximum number of retries for processing a transform job invocation.
InvocationsTimeoutInSeconds (integer) --
The timeout value in seconds for an invocation request. The default value is 600.
InvocationsMaxRetries (integer) --
The maximum number of retries when invocation requests are failing. The default value is 3.
MaxPayloadInMB (integer) --
The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.
BatchStrategy (string) --
Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
Environment (dict) --
The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
(string) --
(string) --
TransformInput (dict) --
Describes the input source of a transform job and the way the transform job consumes it.
DataSource (dict) --
Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
S3DataSource (dict) --
The S3 location of the data source that is associated with a channel.
S3DataType (string) --
If you choose S3Prefix , S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.
If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.
The following values are compatible: ManifestFile , S3Prefix
The following value is not compatible: AugmentedManifestFile
S3Uri (string) --
Depending on the value specified for the S3DataType , identifies either a key name prefix or a manifest. For example:
A key name prefix might look like this: s3://bucketname/exampleprefix .
A manifest might look like this: s3://bucketname/example.manifest The manifest is an S3 object which is a JSON file with the following format: [ {"prefix": "s3://customer_bucket/some/prefix/"}, "relative/path/to/custdata-1", "relative/path/custdata-2", ... "relative/path/custdata-N" ] The preceding JSON matches the following S3Uris : s3://customer_bucket/some/prefix/relative/path/to/custdata-1 s3://customer_bucket/some/prefix/relative/path/custdata-2 ... s3://customer_bucket/some/prefix/relative/path/custdata-N The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.
ContentType (string) --
The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
CompressionType (string) --
If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None .
SplitType (string) --
The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None , which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
RecordIO
TFRecord
When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord , Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord , Amazon SageMaker sends individual records in each request.
Note
Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord . Padding is not removed if the value of BatchStrategy is set to MultiRecord .
For more information about RecordIO , see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord , see Consuming TFRecord data in the TensorFlow documentation.
TransformOutput (dict) --
Describes the results of a transform job.
S3OutputPath (string) --
The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix .
For every S3 object used as input for the transform job, batch transform stores the transformed data with an .``out`` suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv , batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out . Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .``out`` file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
Accept (string) --
The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
AssembleWith (string) --
Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None . To add a newline character at the end of every transformed record, specify Line .
KmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide .
DataCaptureConfig (dict) --
Configuration to control how SageMaker captures inference data for batch transform jobs.
DestinationS3Uri (string) --
The Amazon S3 location being used to capture the data.
KmsKeyId (string) --
The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the batch transform job.
The KmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
GenerateInferenceId (boolean) --
Flag that indicates whether to append inference id to the output.
TransformResources (dict) --
Describes the resources, including ML instance types and ML instance count, to use for transform job.
InstanceType (string) --
The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.large instance types.
InstanceCount (integer) --
The number of ML compute instances to use in the transform job. The default value is 1 , and the maximum is 100 . For distributed transform jobs, specify a value greater than 1 .
VolumeKmsKeyId (string) --
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.
Note
Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage.
For a list of instance types that support local instance storage, see Instance Store Volumes .
For more information about local instance storage encryption, see SSD Instance Store Volumes .
The VolumeKmsKeyId can be any of the following formats:
Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
Alias name: alias/ExampleAlias
Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
CreationTime (datetime) --
A timestamp that shows when the transform Job was created.
TransformStartTime (datetime) --
Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime .
TransformEndTime (datetime) --
Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime .
LabelingJobArn (string) --
The Amazon Resource Name (ARN) of the labeling job that created the transform job.
AutoMLJobArn (string) --
The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
DataProcessing (dict) --
The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records .
InputFilter (string) --
A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want SageMaker to pass the entire input dataset to the algorithm, accept the default value $ .
Examples: "$" , "$[1:]" , "$.features"
OutputFilter (string) --
A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default value, $ . If you specify indexes that aren't within the dimension size of the joined dataset, you get an error.
Examples: "$" , "$[0,5:]" , "$['id','SageMakerOutput']"
JoinSource (string) --
Specifies the source of the data to join with the transformed data. The valid values are None and Input . The default value is None , which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input . You can specify OutputFilter as an additional filter to select a portion of the joined dataset and store it in the output file.
For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput . The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput .
For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file.
For information on how joining in applied, see Workflow for Associating Inferences with Input Records .
ExperimentConfig (dict) --
Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:
ExperimentName (string) --
The name of an existing experiment to associate with the trial component.
TrialName (string) --
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
TrialComponentDisplayName (string) --
The display name for the trial component. If this key isn't specified, the display name is the trial component name.
RunName (string) --
The name of the experiment run to associate with the trial component.
Tags (list) --
A list of tags associated with the transform job.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
MonitoringSchedules (list) --
The monitoring schedules for a model.
(dict) --
A monitoring schedule for a model displayed in the Amazon SageMaker Model Dashboard.
MonitoringScheduleArn (string) --
The Amazon Resource Name (ARN) of a monitoring schedule.
MonitoringScheduleName (string) --
The name of a monitoring schedule.
MonitoringScheduleStatus (string) --
The status of the monitoring schedule.
MonitoringType (string) --
The monitor type of a model monitor.
FailureReason (string) --
If a monitoring job failed, provides the reason.
CreationTime (datetime) --
A timestamp that indicates when the monitoring schedule was created.
LastModifiedTime (datetime) --
A timestamp that indicates when the monitoring schedule was last updated.
MonitoringScheduleConfig (dict) --
Configures the monitoring schedule and defines the monitoring job.
ScheduleConfig (dict) --
Configures the monitoring schedule.
ScheduleExpression (string) --
A cron expression that describes details about the monitoring schedule.
The supported cron expressions are:
If you want to set the job to start every hour, use the following: Hourly: cron(0 * ? * * *)
If you want to start the job daily: cron(0 [00-23] ? * * *)
If you want to run the job one time, immediately, use the following keyword: NOW
For example, the following are valid cron expressions:
Daily at noon UTC: cron(0 12 ? * * *)
Daily at midnight UTC: cron(0 0 ? * * *)
To support running every 6, 12 hours, the following are also supported:
cron(0 [00-23]/[01-24] ? * * *)
For example, the following are valid cron expressions:
Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)
Every two hours starting at midnight: cron(0 0/2 ? * * *)
Note
Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.
We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.
You can also specify the keyword NOW to run the monitoring job immediately, one time, without recurring.
DataAnalysisStartTime (string) --
Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify: "-PT5H" .
The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the DataAnalysisEndTime parameter.
If you set ScheduleExpression to NOW , this parameter is required.
DataAnalysisEndTime (string) --
Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify: "-PT1H" .
The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the DataAnalysisStartTime parameter.
If you set ScheduleExpression to NOW , this parameter is required.
MonitoringJobDefinition (dict) --
Defines the monitoring job.
BaselineConfig (dict) --
Baseline configuration used to validate that the data conforms to the specified constraints and statistics
BaseliningJobName (string) --
The name of the job that performs baselining for the monitoring job.
ConstraintsResource (dict) --
The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
S3Uri (string) --
The Amazon S3 URI for the constraints resource.
StatisticsResource (dict) --
The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
S3Uri (string) --
The Amazon S3 URI for the statistics resource.
MonitoringInputs (list) --
The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
(dict) --
The inputs for a monitoring job.
EndpointInput (dict) --
The endpoint for a monitoring job.
EndpointName (string) --
An endpoint in customer's account which has enabled DataCaptureConfig enabled.
LocalPath (string) --
Path to the filesystem where the endpoint data is available to the container.
S3InputMode (string) --
Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
S3DataDistributionType (string) --
Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to FullyReplicated
FeaturesAttribute (string) --
The attributes of the input data that are the input features.
InferenceAttribute (string) --
The attribute of the input data that represents the ground truth label.
ProbabilityAttribute (string) --
In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute (float) --
The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset (string) --
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
EndTimeOffset (string) --
If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
ExcludeFeaturesAttribute (string) --
The attributes of the input data to exclude from the analysis.
BatchTransformInput (dict) --
Input object for the batch transform job.
DataCapturedDestinationS3Uri (string) --
The Amazon S3 location being used to capture the data.
DatasetFormat (dict) --
The dataset format for your batch transform job.
Csv (dict) --
The CSV dataset used in the monitoring job.
Header (boolean) --
Indicates if the CSV data has a header.
Json (dict) --
The JSON dataset used in the monitoring job
Line (boolean) --
Indicates if the file should be read as a JSON object per line.
Parquet (dict) --
The Parquet dataset used in the monitoring job
LocalPath (string) --
Path to the filesystem where the batch transform data is available to the container.
S3InputMode (string) --
Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
S3DataDistributionType (string) --
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
FeaturesAttribute (string) --
The attributes of the input data that are the input features.
InferenceAttribute (string) --
The attribute of the input data that represents the ground truth label.
ProbabilityAttribute (string) --
In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute (float) --
The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset (string) --
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
EndTimeOffset (string) --
If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
ExcludeFeaturesAttribute (string) --
The attributes of the input data to exclude from the analysis.
MonitoringOutputConfig (dict) --
The array of outputs from the monitoring job to be uploaded to Amazon S3.
MonitoringOutputs (list) --
Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
(dict) --
The output object for a monitoring job.
S3Output (dict) --
The Amazon S3 storage location where the results of a monitoring job are saved.
S3Uri (string) --
A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
LocalPath (string) --
The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
S3UploadMode (string) --
Whether to upload the results of the monitoring job continuously or after the job completes.
KmsKeyId (string) --
The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
MonitoringResources (dict) --
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
ClusterConfig (dict) --
The configuration for the cluster resources used to run the processing job.
InstanceCount (integer) --
The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
InstanceType (string) --
The ML compute instance type for the processing job.
VolumeSizeInGB (integer) --
The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
VolumeKmsKeyId (string) --
The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
MonitoringAppSpecification (dict) --
Configures the monitoring job to run a specified Docker container image.
ImageUri (string) --
The container image to be run by the monitoring job.
ContainerEntrypoint (list) --
Specifies the entrypoint for a container used to run the monitoring job.
(string) --
ContainerArguments (list) --
An array of arguments for the container used to run the monitoring job.
(string) --
RecordPreprocessorSourceUri (string) --
An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
PostAnalyticsProcessorSourceUri (string) --
An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
StoppingCondition (dict) --
Specifies a time limit for how long the monitoring job is allowed to run.
MaxRuntimeInSeconds (integer) --
The maximum runtime allowed in seconds.
Note
The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.
Environment (dict) --
Sets the environment variables in the Docker container.
(string) --
(string) --
NetworkConfig (dict) --
Specifies networking options for an monitoring job.
EnableInterContainerTrafficEncryption (boolean) --
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
EnableNetworkIsolation (boolean) --
Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
VpcConfig (dict) --
Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC .
SecurityGroupIds (list) --
The VPC security group IDs, in the form sg-xxxxxxxx . Specify the security groups for the VPC that is specified in the Subnets field.
(string) --
Subnets (list) --
The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .
(string) --
RoleArn (string) --
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
MonitoringJobDefinitionName (string) --
The name of the monitoring job definition to schedule.
MonitoringType (string) --
The type of the monitoring job definition to schedule.
EndpointName (string) --
The endpoint which is monitored.
MonitoringAlertSummaries (list) --
A JSON array where each element is a summary for a monitoring alert.
(dict) --
Provides summary information about a monitor alert.
MonitoringAlertName (string) --
The name of a monitoring alert.
CreationTime (datetime) --
A timestamp that indicates when a monitor alert was created.
LastModifiedTime (datetime) --
A timestamp that indicates when a monitor alert was last updated.
AlertStatus (string) --
The current status of an alert.
DatapointsToAlert (integer) --
Within EvaluationPeriod , how many execution failures will raise an alert.
EvaluationPeriod (integer) --
The number of most recent monitoring executions to consider when evaluating alert status.
Actions (dict) --
A list of alert actions taken in response to an alert going into InAlert status.
ModelDashboardIndicator (dict) --
An alert action taken to light up an icon on the Model Dashboard when an alert goes into InAlert status.
Enabled (boolean) --
Indicates whether the alert action is turned on.
LastMonitoringExecutionSummary (dict) --
Summary of information about the last monitoring job to run.
MonitoringScheduleName (string) --
The name of the monitoring schedule.
ScheduledTime (datetime) --
The time the monitoring job was scheduled.
CreationTime (datetime) --
The time at which the monitoring job was created.
LastModifiedTime (datetime) --
A timestamp that indicates the last time the monitoring job was modified.
MonitoringExecutionStatus (string) --
The status of the monitoring job.
ProcessingJobArn (string) --
The Amazon Resource Name (ARN) of the monitoring job.
EndpointName (string) --
The name of the endpoint used to run the monitoring job.
FailureReason (string) --
Contains the reason a monitoring job failed, if it failed.
MonitoringJobDefinitionName (string) --
The name of the monitoring job.
MonitoringType (string) --
The type of the monitoring job.
BatchTransformInput (dict) --
Input object for the batch transform job.
DataCapturedDestinationS3Uri (string) --
The Amazon S3 location being used to capture the data.
DatasetFormat (dict) --
The dataset format for your batch transform job.
Csv (dict) --
The CSV dataset used in the monitoring job.
Header (boolean) --
Indicates if the CSV data has a header.
Json (dict) --
The JSON dataset used in the monitoring job
Line (boolean) --
Indicates if the file should be read as a JSON object per line.
Parquet (dict) --
The Parquet dataset used in the monitoring job
LocalPath (string) --
Path to the filesystem where the batch transform data is available to the container.
S3InputMode (string) --
Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File .
S3DataDistributionType (string) --
Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated
FeaturesAttribute (string) --
The attributes of the input data that are the input features.
InferenceAttribute (string) --
The attribute of the input data that represents the ground truth label.
ProbabilityAttribute (string) --
In a classification problem, the attribute that represents the class probability.
ProbabilityThresholdAttribute (float) --
The threshold for the class probability to be evaluated as a positive result.
StartTimeOffset (string) --
If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
EndTimeOffset (string) --
If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs .
ExcludeFeaturesAttribute (string) --
The attributes of the input data to exclude from the analysis.
ModelCard (dict) --
The model card for a model.
ModelCardArn (string) --
The Amazon Resource Name (ARN) for a model card.
ModelCardName (string) --
The name of a model card.
ModelCardVersion (integer) --
The model card version.
ModelCardStatus (string) --
The model card status.
SecurityConfig (dict) --
The KMS Key ID (KMSKeyId ) for encryption of model card information.
KmsKeyId (string) --
A Key Management Service key ID to use for encrypting a model card.
CreationTime (datetime) --
A timestamp that indicates when the model card was created.
CreatedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
LastModifiedTime (datetime) --
A timestamp that indicates when the model card was last updated.
LastModifiedBy (dict) --
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
UserProfileArn (string) --
The Amazon Resource Name (ARN) of the user's profile.
UserProfileName (string) --
The name of the user's profile.
DomainId (string) --
The domain associated with the user.
IamIdentity (dict) --
The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
Arn (string) --
The Amazon Resource Name (ARN) of the IAM identity.
PrincipalId (string) --
The ID of the principal that assumes the IAM identity.
SourceIdentity (string) --
The person or application which assumes the IAM identity.
Tags (list) --
The tags associated with a model card.
(dict) --
A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags .
For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources . For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy .
Key (string) --
The tag key. Tag keys must be unique per resource.
Value (string) --
The tag value.
ModelId (string) --
For models created in SageMaker, this is the model ARN. For models created outside of SageMaker, this is a user-customized string.
RiskRating (string) --
A model card's risk rating. Can be low, medium, or high.
NextToken (string) --
If the result of the previous Search request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request.
{'JupyterLabAppImageConfig': {'FileSystemConfig': {'DefaultGid': 'integer',
'DefaultUid': 'integer',
'MountPath': 'string'}}}
Updates the properties of an AppImageConfig.
See also: AWS API Documentation
Request Syntax
client.update_app_image_config(
AppImageConfigName='string',
KernelGatewayImageConfig={
'KernelSpecs': [
{
'Name': 'string',
'DisplayName': 'string'
},
],
'FileSystemConfig': {
'MountPath': 'string',
'DefaultUid': 123,
'DefaultGid': 123
}
},
JupyterLabAppImageConfig={
'FileSystemConfig': {
'MountPath': 'string',
'DefaultUid': 123,
'DefaultGid': 123
},
'ContainerConfig': {
'ContainerArguments': [
'string',
],
'ContainerEntrypoint': [
'string',
],
'ContainerEnvironmentVariables': {
'string': 'string'
}
}
}
)
string
[REQUIRED]
The name of the AppImageConfig to update.
dict
The new KernelGateway app to run on the image.
KernelSpecs (list) -- [REQUIRED]
The specification of the Jupyter kernels in the image.
(dict) --
The specification of a Jupyter kernel.
Name (string) -- [REQUIRED]
The name of the Jupyter kernel in the image. This value is case sensitive.
DisplayName (string) --
The display name of the kernel.
FileSystemConfig (dict) --
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
MountPath (string) --
The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
DefaultUid (integer) --
The default POSIX user ID (UID). If not specified, defaults to 1000 .
DefaultGid (integer) --
The default POSIX group ID (GID). If not specified, defaults to 100 .
dict
The JupyterLab app running on the image.
FileSystemConfig (dict) --
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
MountPath (string) --
The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user .
DefaultUid (integer) --
The default POSIX user ID (UID). If not specified, defaults to 1000 .
DefaultGid (integer) --
The default POSIX group ID (GID). If not specified, defaults to 100 .
ContainerConfig (dict) --
The configuration used to run the application image container.
ContainerArguments (list) --
The arguments for the container when you're running the application.
(string) --
ContainerEntrypoint (list) --
The entrypoint used to run the application in the container.
(string) --
ContainerEnvironmentVariables (dict) --
The environment variables to set in the container
(string) --
(string) --
dict
Response Syntax
{
'AppImageConfigArn': 'string'
}
Response Structure
(dict) --
AppImageConfigArn (string) --
The Amazon Resource Name (ARN) for the AppImageConfig.
{'AdditionalInferenceSpecificationsToAdd': {'SupportedRealtimeInferenceInstanceTypes': {'ml.c7i.12xlarge',
'ml.c7i.16xlarge',
'ml.c7i.24xlarge',
'ml.c7i.2xlarge',
'ml.c7i.48xlarge',
'ml.c7i.4xlarge',
'ml.c7i.8xlarge',
'ml.c7i.large',
'ml.c7i.xlarge',
'ml.dl1.24xlarge',
'ml.m7i.12xlarge',
'ml.m7i.16xlarge',
'ml.m7i.24xlarge',
'ml.m7i.2xlarge',
'ml.m7i.48xlarge',
'ml.m7i.4xlarge',
'ml.m7i.8xlarge',
'ml.m7i.large',
'ml.m7i.xlarge',
'ml.r7i.12xlarge',
'ml.r7i.16xlarge',
'ml.r7i.24xlarge',
'ml.r7i.2xlarge',
'ml.r7i.48xlarge',
'ml.r7i.4xlarge',
'ml.r7i.8xlarge',
'ml.r7i.large',
'ml.r7i.xlarge',
'ml.trn1n.32xlarge'}}}
Updates a versioned model.
See also: AWS API Documentation
Request Syntax
client.update_model_package(
ModelPackageArn='string',
ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval',
ApprovalDescription='string',
CustomerMetadataProperties={
'string': 'string'
},
CustomerMetadataPropertiesToRemove=[
'string',
],
AdditionalInferenceSpecificationsToAdd=[
{
'Name': 'string',
'Description': 'string',
'Containers': [
{
'ContainerHostname': 'string',
'Image': 'string',
'ImageDigest': 'string',
'ModelDataUrl': 'string',
'ProductId': 'string',
'Environment': {
'string': 'string'
},
'ModelInput': {
'DataInputConfig': 'string'
},
'Framework': 'string',
'FrameworkVersion': 'string',
'NearestModelName': 'string',
'AdditionalS3DataSource': {
'S3DataType': 'S3Object',
'S3Uri': 'string',
'CompressionType': 'None'|'Gzip'
}
},
],
'SupportedTransformInstanceTypes': [
'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge',
],
'SupportedRealtimeInferenceInstanceTypes': [
'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge',
],
'SupportedContentTypes': [
'string',
],
'SupportedResponseMIMETypes': [
'string',
]
},
]
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the model package.
string
The approval status of the model.
string
A description for the approval status of the model.
dict
The metadata properties associated with the model package versions.
(string) --
(string) --
list
The metadata properties associated with the model package versions to remove.
(string) --
list
An array of additional Inference Specification objects to be added to the existing array additional Inference Specification. Total number of additional Inference Specifications can not exceed 15. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
(dict) --
A structure of additional Inference Specification. Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package
Name (string) -- [REQUIRED]
A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
Description (string) --
A description of the additional Inference specification
Containers (list) -- [REQUIRED]
The Amazon ECR registry path of the Docker image that contains the inference code.
(dict) --
Describes the Docker container for the model package.
ContainerHostname (string) --
The DNS host name for the Docker container.
Image (string) -- [REQUIRED]
The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.
If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker .
ImageDigest (string) --
An MD5 hash of the training algorithm that identifies the Docker image used for training.
ModelDataUrl (string) --
The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
Note
The model artifacts must be in an S3 bucket that is in the same region as the model package.
ProductId (string) --
The Amazon Web Services Marketplace product ID of the model package.
Environment (dict) --
The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
(string) --
(string) --
ModelInput (dict) --
A structure with Model Input details.
DataInputConfig (string) -- [REQUIRED]
The input configuration object for the model.
Framework (string) --
The machine learning framework of the model package container image.
FrameworkVersion (string) --
The framework version of the Model Package Container Image.
NearestModelName (string) --
The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .
AdditionalS3DataSource (dict) --
The additional data source that is used during inference in the Docker container for your model package.
S3DataType (string) -- [REQUIRED]
The data type of the additional data source that you specify for use in inference or training.
S3Uri (string) -- [REQUIRED]
The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
CompressionType (string) --
The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
SupportedTransformInstanceTypes (list) --
A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
(string) --
SupportedRealtimeInferenceInstanceTypes (list) --
A list of the instance types that are used to generate inferences in real-time.
(string) --
SupportedContentTypes (list) --
The supported MIME types for the input data.
(string) --
SupportedResponseMIMETypes (list) --
The supported MIME types for the output data.
(string) --
dict
Response Syntax
{
'ModelPackageArn': 'string'
}
Response Structure
(dict) --
ModelPackageArn (string) --
The Amazon Resource Name (ARN) of the model.
{'SpaceSettings': {'AppType': {'Canvas',
'DatasetManager',
'DetailedProfiler',
'Local',
'RSession',
'SageMakerLite',
'Savitur',
'VSCode'}}}
Updates the settings of a space.
See also: AWS API Documentation
Request Syntax
client.update_space(
DomainId='string',
SpaceName='string',
SpaceSettings={
'JupyterServerAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
},
'LifecycleConfigArns': [
'string',
],
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'KernelGatewayAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
},
'CustomImages': [
{
'ImageName': 'string',
'ImageVersionNumber': 123,
'AppImageConfigName': 'string'
},
],
'LifecycleConfigArns': [
'string',
]
},
'CodeEditorAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
}
},
'JupyterLabAppSettings': {
'DefaultResourceSpec': {
'SageMakerImageArn': 'string',
'SageMakerImageVersionArn': 'string',
'SageMakerImageVersionAlias': 'string',
'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge',
'LifecycleConfigArn': 'string'
},
'CodeRepositories': [
{
'RepositoryUrl': 'string'
},
]
},
'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'VSCode'|'Savitur'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSession'|'RSessionGateway'|'Canvas'|'DatasetManager'|'SageMakerLite'|'Local',
'SpaceStorageSettings': {
'EbsStorageSettings': {
'EbsVolumeSizeInGb': 123
}
},
'CustomFileSystems': [
{
'EFSFileSystem': {
'FileSystemId': 'string'
}
},
]
},
SpaceDisplayName='string'
)
string
[REQUIRED]
The ID of the associated Domain.
string
[REQUIRED]
The name of the space.
dict
A collection of space settings.
JupyterServerAppSettings (dict) --
The JupyterServer app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
KernelGatewayAppSettings (dict) --
The KernelGateway app settings.
DefaultResourceSpec (dict) --
The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.
Note
The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the Amazon Web Services Command Line Interface or Amazon Web Services CloudFormation and the instance type parameter value is not passed.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CustomImages (list) --
A list of custom SageMaker images that are configured to run as a KernelGateway app.
(dict) --
A custom SageMaker image. For more information, see Bring your own SageMaker image .
ImageName (string) -- [REQUIRED]
The name of the CustomImage. Must be unique to your account.
ImageVersionNumber (integer) --
The version number of the CustomImage.
AppImageConfigName (string) -- [REQUIRED]
The name of the AppImageConfig.
LifecycleConfigArns (list) --
The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.
Note
To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list.
(string) --
CodeEditorAppSettings (dict) --
The Code Editor application settings.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
JupyterLabAppSettings (dict) --
The settings for the JupyterLab application.
DefaultResourceSpec (dict) --
Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.
SageMakerImageArn (string) --
The ARN of the SageMaker image that the image version belongs to.
SageMakerImageVersionArn (string) --
The ARN of the image version created on the instance.
SageMakerImageVersionAlias (string) --
The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
InstanceType (string) --
The instance type that the image version runs on.
Note
JupyterServer apps only support the system value.
For KernelGateway apps , the system value is translated to ml.t3.medium . KernelGateway apps also support all other values for available instance types.
LifecycleConfigArn (string) --
The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
CodeRepositories (list) --
A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
(dict) --
A Git repository that SageMaker automatically displays to users for cloning in the JupyterServer application.
RepositoryUrl (string) -- [REQUIRED]
The URL of the Git repository.
AppType (string) --
The type of app created within the space.
SpaceStorageSettings (dict) --
The storage settings for a private space.
EbsStorageSettings (dict) --
A collection of EBS storage settings for a private space.
EbsVolumeSizeInGb (integer) -- [REQUIRED]
The size of an EBS storage volume for a private space.
CustomFileSystems (list) --
A file system, created by you, that you assign to a space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
(dict) --
A file system, created by you, that you assign to a user profile or space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
EFSFileSystem (dict) --
A custom file system in Amazon EFS.
FileSystemId (string) -- [REQUIRED]
The ID of your Amazon EFS file system.
string
The name of the space that appears in the Amazon SageMaker Studio UI.
dict
Response Syntax
{
'SpaceArn': 'string'
}
Response Structure
(dict) --
SpaceArn (string) --
The space's Amazon Resource Name (ARN).